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What Is an Observational Study? | Guide & Examples

Published on March 31, 2022 by Tegan George . Revised on December 2, 2022.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables and observer bias impacting your analysis.

Table of contents

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs. experiment, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.

There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies .

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyze a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analyzing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

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observational study and case reports

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for practical or ethical reasons , or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

Overall, it is crucial to stay organized. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or “lurking” variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyze your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive  or deductive approach :

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis .

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.


The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomize your participants safely and your research question is definitely causal in nature, consider using an experiment.

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

When designing the experiment, you decide:

Experimental design is essential to the internal and external validity of your experiment.

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Lesson 1: Introduction to Epidemiology

Section 7: analytic epidemiology.

As noted earlier, descriptive epidemiology can identify patterns among cases and in populations by time, place and person. From these observations, epidemiologists develop hypotheses about the causes of these patterns and about the factors that increase risk of disease. In other words, epidemiologists can use descriptive epidemiology to generate hypotheses, but only rarely to test those hypotheses. For that, epidemiologists must turn to analytic epidemiology.

Key feature of analytic epidemiology = Comparison group

The key feature of analytic epidemiology is a comparison group. Consider a large outbreak of hepatitis A that occurred in Pennsylvania in 2003.( 38 ) Investigators found almost all of the case-patients had eaten at a particular restaurant during the 2–6 weeks (i.e., the typical incubation period for hepatitis A) before onset of illness. While the investigators were able to narrow down their hypotheses to the restaurant and were able to exclude the food preparers and servers as the source, they did not know which particular food may have been contaminated. The investigators asked the case-patients which restaurant foods they had eaten, but that only indicated which foods were popular. The investigators, therefore, also enrolled and interviewed a comparison or control group — a group of persons who had eaten at the restaurant during the same period but who did not get sick. Of 133 items on the restaurant’s menu, the most striking difference between the case and control groups was in the proportion that ate salsa (94% of case-patients ate, compared with 39% of controls). Further investigation of the ingredients in the salsa implicated green onions as the source of infection. Shortly thereafter, the Food and Drug Administration issued an advisory to the public about green onions and risk of hepatitis A. This action was in direct response to the convincing results of the analytic epidemiology, which compared the exposure history of case-patients with that of an appropriate comparison group.

When investigators find that persons with a particular characteristic are more likely than those without the characteristic to contract a disease, the characteristic is said to be associated with the disease. The characteristic may be a:

Identifying factors associated with disease help health officials appropriately target public health prevention and control activities. It also guides additional research into the causes of disease.

Thus, analytic epidemiology is concerned with the search for causes and effects, or the why and the how. Epidemiologists use analytic epidemiology to quantify the association between exposures and outcomes and to test hypotheses about causal relationships. It has been said that epidemiology by itself can never prove that a particular exposure caused a particular outcome. Often, however, epidemiology provides sufficient evidence to take appropriate control and prevention measures.

Epidemiologic studies fall into two categories: experimental and observational .

Experimental studies

In an experimental study, the investigator determines through a controlled process the exposure for each individual (clinical trial) or community (community trial), and then tracks the individuals or communities over time to detect the effects of the exposure. For example, in a clinical trial of a new vaccine, the investigator may randomly assign some of the participants to receive the new vaccine, while others receive a placebo shot. The investigator then tracks all participants, observes who gets the disease that the new vaccine is intended to prevent, and compares the two groups (new vaccine vs. placebo) to see whether the vaccine group has a lower rate of disease. Similarly, in a trial to prevent onset of diabetes among high-risk individuals, investigators randomly assigned enrollees to one of three groups — placebo, an anti-diabetes drug, or lifestyle intervention. At the end of the follow-up period, investigators found the lowest incidence of diabetes in the lifestyle intervention group, the next lowest in the anti-diabetic drug group, and the highest in the placebo group.( 39 )

Observational studies

In an observational study, the epidemiologist simply observes the exposure and disease status of each study participant. John Snow’s studies of cholera in London were observational studies. The two most common types of observational studies are cohort studies and case-control studies; a third type is cross-sectional studies.

Cohort study. A cohort study is similar in concept to the experimental study. In a cohort study the epidemiologist records whether each study participant is exposed or not, and then tracks the participants to see if they develop the disease of interest. Note that this differs from an experimental study because, in a cohort study, the investigator observes rather than determines the participants’ exposure status. After a period of time, the investigator compares the disease rate in the exposed group with the disease rate in the unexposed group. The unexposed group serves as the comparison group, providing an estimate of the baseline or expected amount of disease occurrence in the community. If the disease rate is substantively different in the exposed group compared to the unexposed group, the exposure is said to be associated with illness.

The length of follow-up varies considerably. In an attempt to respond quickly to a public health concern such as an outbreak, public health departments tend to conduct relatively brief studies. On the other hand, research and academic organizations are more likely to conduct studies of cancer, cardiovascular disease, and other chronic diseases which may last for years and even decades. The Framingham study is a well-known cohort study that has followed over 5,000 residents of Framingham, Massachusetts, since the early 1950s to establish the rates and risk factors for heart disease.( 7 ) The Nurses Health Study and the Nurses Health Study II are cohort studies established in 1976 and 1989, respectively, that have followed over 100,000 nurses each and have provided useful information on oral contraceptives, diet, and lifestyle risk factors.( 40 ) These studies are sometimes called follow-up or prospective cohort studies, because participants are enrolled as the study begins and are then followed prospectively over time to identify occurrence of the outcomes of interest.

An alternative type of cohort study is a retrospective cohort study. In this type of study both the exposure and the outcomes have already occurred. Just as in a prospective cohort study, the investigator calculates and compares rates of disease in the exposed and unexposed groups. Retrospective cohort studies are commonly used in investigations of disease in groups of easily identified people such as workers at a particular factory or attendees at a wedding. For example, a retrospective cohort study was used to determine the source of infection of cyclosporiasis, a parasitic disease that caused an outbreak among members of a residential facility in Pennsylvania in 2004.( 41 ) The investigation indicated that consumption of snow peas was implicated as the vehicle of the cyclosporiasis outbreak.

Case-control study. In a case-control study, investigators start by enrolling a group of people with disease (at CDC such persons are called case-patients rather than cases, because case refers to occurrence of disease, not a person). As a comparison group, the investigator then enrolls a group of people without disease (controls). Investigators then compare previous exposures between the two groups. The control group provides an estimate of the baseline or expected amount of exposure in that population. If the amount of exposure among the case group is substantially higher than the amount you would expect based on the control group, then illness is said to be associated with that exposure. The study of hepatitis A traced to green onions, described above, is an example of a case-control study. The key in a case-control study is to identify an appropriate control group, comparable to the case group in most respects, in order to provide a reasonable estimate of the baseline or expected exposure.

Cross-sectional study. In this third type of observational study, a sample of persons from a population is enrolled and their exposures and health outcomes are measured simultaneously. The cross-sectional study tends to assess the presence (prevalence) of the health outcome at that point of time without regard to duration. For example, in a cross-sectional study of diabetes, some of the enrollees with diabetes may have lived with their diabetes for many years, while others may have been recently diagnosed.

From an analytic viewpoint the cross-sectional study is weaker than either a cohort or a case-control study because a cross-sectional study usually cannot disentangle risk factors for occurrence of disease (incidence) from risk factors for survival with the disease. (Incidence and prevalence are discussed in more detail in Lesson 3.) On the other hand, a cross-sectional study is a perfectly fine tool for descriptive epidemiology purposes. Cross-sectional studies are used routinely to document the prevalence in a community of health behaviors (prevalence of smoking), health states (prevalence of vaccination against measles), and health outcomes, particularly chronic conditions (hypertension, diabetes).

In summary, the purpose of an analytic study in epidemiology is to identify and quantify the relationship between an exposure and a health outcome. The hallmark of such a study is the presence of at least two groups, one of which serves as a comparison group. In an experimental study, the investigator determines the exposure for the study subjects; in an observational study, the subjects are exposed under more natural conditions. In an observational cohort study, subjects are enrolled or grouped on the basis of their exposure, then are followed to document occurrence of disease. Differences in disease rates between the exposed and unexposed groups lead investigators to conclude that exposure is associated with disease. In an observational case-control study, subjects are enrolled according to whether they have the disease or not, then are questioned or tested to determine their prior exposure. Differences in exposure prevalence between the case and control groups allow investigators to conclude that the exposure is associated with the disease. Cross-sectional studies measure exposure and disease status at the same time, and are better suited to descriptive epidemiology than causation.

Exercise 1.7

Classify each of the following studies as:

Check your answer.

References (This Section)

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Study Design 101

Case Report

An article that describes and interprets an individual case, often written in the form of a detailed story. Case reports often describe:

Case reports are considered the lowest level of evidence, but they are also the first line of evidence, because they are where new issues and ideas emerge. This is why they form the base of our pyramid. A good case report will be clear about the importance of the observation being reported.

If multiple case reports show something similar, the next step might be a case-control study to determine if there is a relationship between the relevant variables.


Design pitfalls to look out for

The patient should be described in detail, allowing others to identify patients with similar characteristics.

Does the case report provide information about the patient's age, sex, ethnicity, race, employment status, social situation, medical history, diagnosis, prognosis, previous treatments, past and current diagnostic test results, medications, psychological tests, clinical and functional assessments, and current intervention?

Case reports should include carefully recorded, unbiased observations.

Does the case report include measurements and/or recorded observations of the case? Does it show a bias?

Case reports should explore and infer, not confirm, deduce, or prove. They cannot demonstrate causality or argue for the adoption of a new treatment approach.

Does the case report present a hypothesis that can be confirmed by another type of study?

Fictitious Example

A physician treated a young and otherwise healthy patient who came to her office reporting numbness all over her body. The physician could not determine any reason for this numbness and had never seen anything like it. After taking an extensive history the physician discovered that the patient had recently been to the beach for a vacation and had used a very new type of spray sunscreen. The patient had stored the sunscreen in her cooler at the beach because she liked the feel of the cool spray in the hot sun. The physician suspected that the spray sunscreen had undergone a chemical reaction from the coldness which caused the numbness. She also suspected that because this is a new type of sunscreen other physicians may soon be seeing patients with this numbness.

The physician wrote up a case report describing how the numbness presented, how and why she concluded it was the spray sunscreen, and how she treated the patient. Later, when other doctors began seeing patients with this numbness, they found this case report helpful as a starting point in treating their patients.

Real-life Examples

Hymes KB. Cheung T. Greene JB. Prose NS. Marcus A. Ballard H. William DC. Laubenstein LJ. (1981). Kaposi's sarcoma in homosexual men-a report of eight cases. Lancet. 2(8247), 598-600.

This case report was published by eight physicians in New York city who had unexpectedly seen eight male patients with Kaposi’s sarcoma (KS). Prior to this, KS was very rare in the U.S. and occurred primarily in the lower extremities of older patients. These cases were decades younger, had generalized KS, and a much lower rate of survival. This was before the discovery of HIV or the use of the term AIDS and this case report was one of the first published items about AIDS patients.

Wu, E. B., & Sung, J. J. Y. (2003). Haemorrhagic-fever-like changes and normal chest radiograph in a doctor with SARS. Lancet, 361(9368), 1520-1521.

This case report is written by the patient, a physician who contracted SARS, and his colleague who treated him, during the 2003 outbreak of SARS in Hong Kong. They describe how the disease progressed in Dr. Wu and based on Dr. Wu’s case, advised that a chest CT showed hidden pneumonic changes and facilitate a rapid diagnosis.

Related Terms

Case Series

A report about a small group of similar cases.

Preplanned Case-Observation

A case in which symptoms are elicited to study disease mechanisms. (Ex. Having a patient sleep in a lab to do brain imaging for a sleep disorder).

Now test yourself!

1. Case studies are not considered evidence-based even though the authors have studied the case in great depth.

a) True b) False

2. When are Case reports most useful?

a) When you encounter common cases and need more information b) When new symptoms or outcomes are unidentified c) When developing practice guidelines d) When the population being studied is very large

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observational study and case reports

PH717 Module 1B - Descriptive Tools

Descriptive epidemiology & descriptive statistics.

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Case Reports and Case Series

Case reports, case series, test yourself.

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Categories of Descriptive Epidemiology

A case report is a detailed description of disease occurrence in a single person. Unusual features of the case may suggest a new hypothesis about the causes or mechanisms of disease.

Example: Acquired Immunodeficiency in an Infant; Possible Transmission by Means of Blood Products

In April 1983 it had not yet been shown that AIDS could be transmitted by blood or blood products. An infant born with Rh incompatibility; required blood products from 18 donors over 8 weeks and subsequently developed unusual recurrent infections with opportunistic agents such as Candida. The infant's T cell count was low, suggesting AIDS. There was no family history of immunodeficiency, but one of the blood donors was found to have died of AIDS. This led the investigators to hypothesize that AIDS could be transmitted by blood transfusion.

Link to article by Ammann AJ et al: Acquired immunodeficiency in an infant: possible transmission by means of blood products. The Lancet 1:956-958, 1983.

A case series is a report on the characteristics of a group of subjects who all have a particular disease or condition. Common features among the group may suggest hypotheses about disease causation. Note that the "series" may be small (as in the example below) or it may be large (hundreds or thousands of "cases"). However, the chief limitation is that there is no comparison group. Consequently, common features may suggest hypotheses, but these need to be tested with some sort of analytical study before an association can be accepted as valid.

Example: Discovery of HIV in the United States

observational study and case reports

This was an extraordinarily important case series (a detailed description of characteristics of a series of people who all have the same disease) that suggested that this new syndrome was associated with sexual activity in male homosexuals. Alerting the medical establishment and proposing a hypothesis was an important milestone in the AIDS epidemic.

Link to article by Gottlieb MS, et al: Pneumocystis carinii pneumonia and mucosal candidiasis in previously healthy homosexual men: evidence of a new acquired cellular immunodeficiency. N Engl J Med 1981;305:1425-1431.

There had been a number of case reports of liver cancers in young women taking oral contraceptives. A study was undertaken by contacting all of the cancer registries collaborating with the American College of Surgeons. The investigators wanted to collect information on as many of these rare liver tumors as possible across the US.  

Table - Oral Contraceptive Use Among Women Who Developed Liver Cancer

What conclusions can you draw from these data regarding a possible increased risk of liver cancer in woman taking oral contraceptives? Think about it before you look at the answer.

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Content ©2020. All Rights Reserved. Date last modified: September 10, 2020. Wayne W. LaMorte, MD, PhD, MPH


Strengthening the reporting of observational studies in epidemiology

What is STROBE?

STROBE stands for an international, collaborative initiative of epidemiologists, methodologists, statisticians, researchers and journal editors involved in the conduct and dissemination of observational studies, with the common aim of  STrengthening the Reporting of OBservational studies in Epidemiology .

For STROBE-related entries in PubMed click  here .

Aims and use of STROBE

Incomplete and inadequate reporting of research hampers the assessment of the strengths and weaknesses of the studies reported in the medical literature. Readers need to know what was planned (and what was not), what was done, what was found, and what the results mean. Recommendations on the reporting of studies that are endorsed by leading medical journals can improve the quality of reporting.

Observational research comprises several study designs and many topic areas. We aimed to establish a checklist of items that should be included in articles reporting such research – the STROBE Statement. We considered it reasonable to initially restrict the recommendations to the three main analytical designs that are used in observational research: cohort, case-control, and cross-sectional studies. We want to provide guidance on how to report observational research well. Our recommendations are not prescriptions for designing or conducting studies. Also, the checklist is not an instrument to evaluate the quality of observational research.

Further use

The STROBE initiative should be seen as an ongoing process, with future revisions of the recommendations based on comments, critique and new evidence. We welcome translations into other languages and extensions to other observational study designs, for example nested case-control studies, and specific topic areas, for example, genetic and molecular epidemiology.

Note : We ask anyone intending to use the STROBE Statement for further extensions, translations or other STROBE-related work to contact the coordinating group through this website first. This will allow to coordinate efforts and to avoid duplication. The authors of the original  STROBE articles  hold the copyright. Please, contact us if you wish to re-publish STROBE material in additional journals, books or other media.

All documents and publications produced by the STROBE Initiative are open-access and available for download on this website.

Observational Studies I Case Report, Case Series, Case-Control Study

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3.2 - controlled clinical trials compared to observational studies.

Medical research, as a scientific investigation, is based on careful observation and theory. Theory directs the observation and provides a basis for interpreting the results. The strength of the evidence from a clinical study is proportional to amount of the control of bias and variability when the study was conducted as well as the magnitude of the observed effect. Clinical studies can be characterized as uncontrolled observations, observational comparative and controlled clinical trials.

Case reports and case-series are uncontrolled observational studies.

Case Report

A case report only demonstrates that a clinical event of interest is possible. In a case report, there is no control of treatment assignment, endpoint ascertainment, or confounders. There is no control group for the sake of comparison. The report is descriptive in nature, not a formal statistical analysis.

Case reports are useful in generating hypotheses for future testing. For example, a physician may report that a patient in his practice, who was taking a specific anorexic drug, developed primary pulmonary hypertension (PPH), a rare condition that occurs in 1-2 out of every million Americans. Is this convincing evidence that the anorexic drug causes PPH?

Case Series

A case series carries more weight than a single case report, but cannot prove the efficacy of a treatment. Case series and case reports are susceptible to large selection biases. Consider the example of laetrile, an apricot pit extract that was reputed to cure cancer. Seven case series were reported; the strength of evidence from these studies has been summarized by the US National Cancer Institute ( NCI ). While a proportion of patients may have experienced spontaneous remission of cancer, rigorous testing in controlled environments was never performed. After an estimated 70,000 patients had been treated, the NCI undertook a retrospective analysis of laetrile only to decide no definite conclusions supporting anti-cancer activity could be made ( Special Report on Laetrile: The NCI Laetrile Review ). The Cochrane review on laetrile (2015), states, “ there is no reliable evidence for the alleged effects of laetrile or amygdalin for curative effects in cancer patients. ” Based on a series of reported cases, many believed laetrile would cure their cancer, perhaps refusing other effective treatments, and subjecting themselves to adverse effects of cyanide, for many years, this continued for many years with anti-tumor efficacy of laetrile unsupported while associated adverse effects were coming to light.

Database Analysis

A database analysis is similar to a case series but may have a control group, depending on the data source. The source and quality of the data used for this secondary analysis are key. If the analysis attempts to evaluate treatment differences from data in which treatment assignment was based on physician and patient discretion, nonrandomized and open-label, bias is likely.

Databases are best used to study patterns with exploratory statistical analyses. For example, the NIH sponsored a database analysis of interstitial cystitis (IC) during the 1990s. This consisted of data from over 400 individuals with IC who underwent various and numerous therapies for their condition. The objective of the database analysis was to determine if there were patterns of treatments that may be effective in treating the disease. (Rovner et al. 2000).

As another example, in the case of genomic research, specific data mining tools have been developed to search for patterns in large databases of genetic data, leading to the discovery of particular candidate genes.

Epidemiologic Study

An epidemiologic study is often a case-control or a cohort design, both comparative observational studies. An observational study lacks the key component of an experiment, namely, control over treatment assignment. Commonly these designs are used in assessing the influence of risk factors for a disease. Subjects meeting entrance criteria may have been identified through a database search. The choice of the control group is a crucial design component in observational studies.

Case-Control Study

In a case-control study , the investigator identifies cases (subjects with the disease) and controls (subjects without the disease) and retrospectively assesses some type of treatment or exposure. Because the investigator has selected the cases and controls, relative risk cannot be calculated directly from a case-control study.

In addition, levels of treatment or exposure may be recorded based on a subject’s recall of events that occurred many years previously, thus recall bias,(systematic differences in accuracy or completeness of recall) can affect the study results.

Prospective Cohort Study

In a prospective cohort study , individuals are followed forward in time with subsequent evaluations to determine which individuals develop into cases. The relationship of specific risk factors that were measured at baseline with the subsequent outcome is assessed. The cohort study may consist of one or more samples with particular risk factors, called cohorts . It is possible to control some sources of bias in a prospective cohort study by following standard procedures in collecting data and ascertaining endpoints. Since the subjects are not assigned risk factors in a randomized manner, however, there may remain covariates that are confounded with a risk factor. Sometimes, a particular treatment group (or groups) from a randomized trial is followed as a cohort, providing a cohort in which the treatment was assigned at random.

Prospective studies tend to have fewer design problems and less bias than retrospective studies, but they are more expensive with respect to time and cost.

An example of a case-control study: A cardiologist identifies 36 patients currently in his practice with a specific form of cardiac valve disease. He identifies another group of relatively healthy patients and matches two of them to each of the patients with cardiac valve disease according to age (± 5years) and BMI (± 2.5). He plans to interview all 36 + 72 = 108 patients to assess their use of diet drugs during the past ten years.

A classic example of a cohort study: U.S. National Heart Lung and Blood Institute Framingham Heart Study

Piantodosi (2005) lists the following conditions for convincing non-experimental comparative studies:

Controlled Clinical Trial

A controlled clinical trial contains all of the key components of a true experimental design. Treatments are assigned by design; administration of treatment and endpoint ascertainment follows a protocol. When properly designed and conducted, especially with the use of randomization and masking, the controlled clinical trial instills confidence that bias has been minimized. Replication of a controlled clinical trial, if congruent with the results of the first clinical trial, provides verification.

Observational Studies: Cohort and Case-Control Studies : Plastic and Reconstructive Surgery

observational study and case reports

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Observational Studies: Cohort and Case-Control Studies

Song, Jae W. M.D.; Chung, Kevin C. M.D., M.S.

Ann Arbor, Mich.

From the Section of Plastic Surgery, Department of Surgery, University of Michigan Health System.

Received for publication January 27, 2010; accepted March 5, 2010.

Disclosure: The authors have no financial interest to declare in relation to the content of this article.

Kevin C. Chung, M.D., M.S.; Section of Plastic Surgery; University of Michigan Health System; 1500 East Medical Center Drive; 2130 Taubman Center, SPC 5340; Ann Arbor, Mich. 48109-5340; [email protected]


Observational studies constitute an important category of study designs. To address some investigative questions in plastic surgery, randomized controlled trials are not always indicated or ethical to conduct. Instead, observational studies may be the next best method of addressing these types of questions. Well-designed observational studies have been shown to provide results similar to those of randomized controlled trials, challenging the belief that observational studies are second rate. Cohort studies and case-control studies are two primary types of observational studies that aid in evaluating associations between diseases and exposures. In this review article, the authors describe these study designs and methodologic issues, and provide examples from the plastic surgery literature.

Because of the innovative nature of the specialty, plastic surgeons are frequently confronted with a spectrum of clinical questions by patients who inquire about “best practices.” It is thus essential that plastic surgeons know how to critically appraise the literature to understand and practice evidence-based medicine and also contribute to the effort by carrying out high-quality investigations. 1 Well-designed randomized controlled trials have held the preeminent position in the hierarchy of evidence-based medicine as level I evidence ( Table 1 ). However, randomized controlled trial methodology, which was first developed for drug trials, can be difficult to conduct for surgical investigations. 2 Instead, well-designed observational studies, recognized as level II or III evidence, can play an important role in deriving evidence for plastic surgery. Results from observational studies are often criticized for being vulnerable to the influence of unpredictable confounding factors. However, recent work has challenged this notion, showing comparable results between observational studies and randomized controlled trials. 3,4 Observational studies can also complement randomized controlled trials in hypothesis generation, establishing questions for future randomized controlled trials, and defining clinical conditions.


Observational studies fall under the category of analytic study designs and are further subclassified as observational or experimental study designs ( Fig. 1 ). The goal of analytic studies is to identify and evaluate causes or risk factors of diseases or health-related events. The differentiating characteristic between observational and experimental study designs is that in the latter, the presence or absence of undergoing an intervention defines the groups. By contrast, in an observational study, the investigator does not intervene and rather simply “observes” and assesses the strength of the relationship between an exposure and disease variable. 5 Three types of observational studies include cohort studies, case-control studies, and cross-sectional studies ( Fig. 1 ). Case-control and cohort studies offer specific advantages by measuring disease occurrence and its association with an exposure by offering a temporal dimension (i.e., prospective or retrospective study design). Cross-sectional studies, also known as prevalence studies, examine the data on disease and exposure at one particular time point ( Fig. 2 ). 5 Because the temporal relationship between disease occurrence and exposure cannot be established, cross-sectional studies cannot assess the cause-and-effect relationship. In this review, we will primarily discuss cohort and case-control study designs and related methodologic issues.



The term “cohort” is derived from the Latin word cohors . Roman legions were composed of 10 cohorts. During battle, each cohort, or military unit, consisting of a specific number of warriors and commanding centurions, were traceable. The word cohort has since been adopted into epidemiology to define a set of people followed over a period of time. W. H. Frost, an epidemiologist from the early 1900s, was the first to use the word cohort in his 1935 publication assessing age-specific mortality rates and tuberculosis. 6 The modern epidemiologic definition of the word now means a “group of people with defined characteristics who are followed up to determine incidence of, or mortality from, some specific disease, all causes of death, or some other outcome.” 6

Study Design

A well-designed cohort study can provide powerful results. In a cohort study, an outcome or disease-free study population is first identified by the exposure or event of interest and followed in time until the disease or outcome of interest occurs ( Fig. 3 , above ). Because exposure is identified before the outcome, cohort studies have a temporal framework to assess causality and thus have the potential to provide the strongest scientific evidence. 7 Advantages and disadvantages of a cohort study are listed in Table 2 . 8,9 Cohort studies are particularly advantageous for examining rare exposures because subjects are selected by their exposure status. In addition, the investigator can examine multiple outcomes simultaneously. Disadvantages include the need for a large sample size and the potentially long follow-up duration of the study design, resulting in a costly endeavor.


Cohort studies can be prospective or retrospective ( Fig. 2 ). Prospective studies are carried out from the present time into the future. Because prospective studies are designed with specific data collection methods, they have the advantage of being tailored to collect specific exposure data and may be more complete. The disadvantage of a prospective cohort study may be the long follow-up period while waiting for events or diseases to occur. Thus, this study design is inefficient for investigating diseases with long latency periods and is vulnerable to a high rate of loss to follow-up. Although prospective cohort studies are invaluable as exemplified by the landmark Framingham Heart Study, started in 1948 and still ongoing, 10 in the plastic surgery literature, this study design is generally seen to be inefficient and impractical. Instead, retrospective cohort studies are better indicated given the timeliness and inexpensive nature of the study design.

Retrospective cohort studies, also known as historical cohort studies, are carried out at the present time and look to the past to examine medical events or outcomes. In other words, a cohort of subjects selected based on exposure status is chosen at the present time, and outcome data (i.e., disease status, event status), which were measured in the past, are reconstructed for analysis. The primary disadvantage of this study design is the limited control the investigator has over data collection. The existing data may be incomplete, inaccurate, or inconsistently measured between subjects. 8 However, because of the immediate availability of the data, this study design is comparatively less costly and shorter than prospective cohort studies. For example, Spear and colleagues examined the effect of obesity and complication rates after undergoing pedicled transverse rectus abdominis musculocutaneous (TRAM) flap reconstruction by retrospectively reviewing 224 pedicled TRAM flaps in 200 patients over a 10-year period. 11 In this example, subjects who underwent pedicled TRAM flap reconstruction were selected and categorized into cohorts by their exposure status: normal/underweight, overweight, or obese. The outcomes of interest were various flap and donor-site complications. The findings revealed that obese patients had a significantly higher incidence of donor-site complications, multiple flap complications, and partial flap necrosis than normal or overweight patients. An advantage of the retrospective study design analysis is the immediate access to the data. A disadvantage is the limited control over the data collection because data were gathered retrospectively over 10 years; for example, a limitation reported by the authors is that mastectomy flap necrosis was not uniformly recorded for all subjects. 11

An important distinction lies between cohort studies and case series. The distinguishing feature between these two types of studies is the presence of a control, or unexposed, group. Contrasting with epidemiologic cohort studies, case series are descriptive studies following one small group of subjects. In essence, they are extensions of case reports. Usually, the cases are obtained from the authors' experiences, generally involve a small number of patients, and more importantly, lack a control group. 12 There is often confusion in designating studies as “cohort studies” when only one group of subjects is examined. Yet, unless a second comparative group serving as a control is present, these studies are defined as case series. The next step in strengthening an observation from a case series is selecting appropriate control groups to conduct a cohort or case-control study, the latter of which is discussed in the Case-Control Studies section later in the article. 9

Methodologic Issues

Selection of subjects in cohort studies.

The hallmark of a cohort study is defining the selected group of subjects by exposure status at the start of the investigation. A critical characteristic of subject selection is to have both the exposed and unexposed groups be selected from the same source population ( Fig. 4 ). 9 Subjects who are not at risk for developing the outcome should be excluded from the study. The source population is determined by practical considerations, such as sampling. Subjects may be effectively sampled from the hospital, be members of a community, or be from a doctor's individual practice. A subset of these subjects will be eligible for the study.


Attrition Bias (Loss to Follow-Up)

Because prospective cohort studies may require long follow-up periods, it is important to minimize loss to follow-up. Loss to follow-up is a situation in which the investigator loses contact with the subject, resulting in missing data. If too many subjects are lost to follow-up, the internal validity of the study is reduced. A general rule of thumb requires that the loss to follow-up rate not exceed 20 percent of the sample. 5 Any systematic differences related to the outcome or exposure of risk factors between those who drop out and those who stay in the study must be examined, if possible, by comparing individuals who remain in the study and those who were lost to follow-up or dropped out. It is therefore important to select subjects who can be followed for the entire duration of the cohort study. Methods of minimizing loss to follow-up are listed in Table 3 .



Case-control studies were historically borne out of interest in the cause of disease. The conceptual basis of the case-control study is similar to taking a history and physical examination; the patient with disease is questioned and examined, and elements from this history taking are knitted together to reveal characteristics or factors that predisposed the patient to the disease. In fact, the practice of interviewing patients about behaviors and conditions preceding illness dates back to the Hippocratic writings of the fourth century BC . 6

Reasons of practicality and feasibility inherent in the study design typically dictate whether a cohort study or case-control study is appropriate. This study design was first recognized in Janet Lane-Claypon's study of breast cancer in 1926, revealing the finding that low fertility rates raise the risk of breast cancer. 13,14 In the ensuing decades, case-control study methodology crystallized with the landmark publication linking smoking and lung cancer in the 1950s. 15 Since that time, retrospective case-control studies have become more prominent in the biomedical literature with more rigorous methodologic advances in design, execution, and analysis.

Case-control studies identify subjects by outcome status at the outset of the investigation. Outcomes of interest may be whether the subject has undergone a specific type of surgery, experienced a complication, or been diagnosed with a disease ( Fig. 3 , below ). Once outcome status is identified and subjects are categorized as cases, controls (subjects without the outcome but from the same source population) are selected. Data regarding exposure to a risk factor or several risk factors are then collected retrospectively, typically by interview, abstraction from records, or survey. Case-control studies are well suited to investigate rare outcomes or outcomes with a long latency period because subjects are selected from the outset by their outcome status. Thus, in comparison with cohort studies, case-control studies are quick and relatively inexpensive to implement, require comparatively fewer subjects, and allow for multiple exposures or risk factors to be assessed for one outcome ( Table 4 ). 8,9


An example of a case-control investigation is the study by Zhang and colleagues, who examined the association of environmental and genetic factors associated with rare congenital microtia, 16 which has an estimated prevalence of 0.83 to 17.4 in 10,000. 17 They selected 121 congenital microtia cases based on clinical phenotype, and 152 unaffected controls, matched by age and sex in the same hospital and same period. Controls were of Han Chinese origin from Jiangsu, China, the same area from which the cases were selected. This allowed both the controls and cases to have the same genetic background, which is important to note given the investigated association between genetic factors and congenital microtia. To examine environmental factors, a questionnaire was administered to the mothers of both cases and controls. The authors concluded that adverse maternal health was among the main risk factors for congenital microtia, specifically, maternal disease during pregnancy (odds ratio, 5.89; 95 percent confidence interval, 2.36 to 14.72), maternal toxicity exposure during pregnancy (odds ratio, 4.76; 95 percent confidence interval, 1.66 to 13.68), and resident area, such as living near industries associated with air pollution (odds ratio, 7.00; 95 percent confidence interval, 2.09 to 23.47). 16 A case-control study design is most efficient for this investigation, given the rarity of the disease outcome. Because congenital microtia is thought to have multifactorial causes, an additional advantage of the case-control study design in this example is the ability to examine multiple exposures and risk factors.

Selection of Cases

Sampling in a case-control study design begins with selecting the cases. In a case-control study, it is imperative that the investigator has explicitly defined inclusion and exclusion criteria before the selection of cases. For example, if the outcome is having a disease, specific diagnostic criteria, disease subtype, stage of disease, or degree of severity should be defined. Such criteria ensure that all the cases are homogenous. Second, cases may be selected from a variety of sources, including hospital patients, clinic patients, or community subjects. Many communities maintain registries of patients with certain diseases and can serve as a valuable source of cases. However, despite the methodologic convenience of this method, validity issues may arise. For example, if cases are selected from one hospital, identified risk factors may be unique to that single hospital. This methodologic choice may weaken the generalizability of the study findings. Another example is choosing cases from the hospital versus the community; most likely, cases from the hospital sample will represent a more severe form of the disease than those in the community. 8 Finally, it is also important to select cases that are representative of cases in the target population to strengthen the study's external validity ( Fig. 4 ). Potential reasons why cases from the original target population eventually filter through and are available as cases (study participants) for a case-control study are illustrated in Figure 5 .


Selection of Controls

Selecting the appropriate group of controls can be one of the most demanding aspects of a case-control study. An important principle is that the distribution of exposure should be the same among cases and controls; in other words, both cases and controls should stem from the same source population. The investigator may also consider the control group to be an at-risk population, with the potential to develop the outcome. Because the validity of the study depends on the comparability of these two groups, cases and controls should otherwise meet the same inclusion criteria in the study.

A case-control study design that exemplifies this methodologic feature is the study by Chung and colleagues, who examined maternal cigarette smoking during pregnancy and the risk of newborns developing cleft lip–cleft palate. 18 A salient feature of this study is the use of the 1996 U.S. Natality database, a population database from which both cases and controls were selected. This database provides a large sample size to assess newborn development of cleft lip–cleft palate (outcome), which has a reported incidence of one in 1000 live births, 19 and also enabled the investigators to choose controls (i.e., healthy newborns) that were generalizable to the general population to strengthen the study's external validity. A significant relationship with maternal cigarette smoking and cleft lip–cleft palate in the newborn was reported in this study (adjusted odds ratio, 1.34; 95 percent confidence interval, 1.36 to 1.76). 18

Matching is a method used in an attempt to ensure comparability between cases and controls and reduces variability and systematic differences attributable to background variables that are not of interest to the investigator. 7 Each case is typically paired individually with a control subject with respect to the background variables. The exposure to the risk factor of interest is then compared between the cases and the controls. This matching strategy is called individual matching. Age, sex, and race are often used to match cases and controls because they are typically strong confounders of disease. 20 Confounders are variables associated with the risk factor and may potentially be a cause of the outcome. 7 Table 5 lists several advantages and disadvantages with a matching design.


Multiple Controls

Investigations examining rare outcomes may have a limited number of cases from which to select, whereas the source population from which controls can be selected is much larger. In such scenarios, the study may be able to provide more information if multiple controls per case are selected. This method increases the “statistical power” of the investigation by increasing the sample size. The precision of the findings may improve by having up to approximately three or four controls per case. 21–23

Bias in Case-Control Studies

Evaluating exposure status can be the Achilles heel of case-control studies. Because information about exposure is typically collected by self-report, interview, or from recorded information, it is susceptible to recall bias, interviewer bias, or will rely on the completeness or accuracy of recorded information, respectively. These biases decrease the internal validity of the investigation and should be carefully addressed and reduced in the study design. Recall bias occurs when a differential response between cases and controls occurs. The common scenario is when a subject with disease (case) will unconsciously recall and report an exposure with better clarity because of the disease experience. Interviewer bias occurs when the interviewer asks leading questions or has an inconsistent interview approach between cases and controls. A good study design will implement a standardized interview in a nonjudgmental atmosphere with well-trained interviewers to reduce interviewer bias. 9


In 2004, the first meeting of the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) group took place in Bristol, United Kingdom. 24 The aim of the group was to establish guidelines on reporting observational research to improve the transparency of the methods, thereby facilitating the critical appraisal of a study's findings. A well-designed but poorly reported study is disadvantaged in contributing to the literature because the results and generalizability of the findings may be difficult to assess. Thus, a 22-item checklist was generated to enhance the reporting of observational studies across disciplines. 25,26 This checklist is also located at the following Web site: http://www.strobe-statement.org . This statement is applicable to cohort studies, case-control studies, and cross-sectional studies. In fact, 18 of the checklist items are common to all three types of observational studies, and four items are specific to each of the three specific study designs. In an effort to provide specific guidance to go along with this checklist, an “explanation and elaboration” article was published for users to better appreciate each item on the checklist. 27 Plastic surgery investigators should peruse this checklist before designing their study and when they are writing up the report for publication. In fact, some journals now require authors to follow the STROBE Statement. A list of participating journals can be found on this Web site: http://www.strobe-statement.org./index.php?id=strobe-endorsement .


Because of the limitations in carrying out randomized controlled trials in surgical investigations, observational studies are becoming more popular for investigating the relationship between exposures, such as risk factors or surgical interventions, and outcomes, such as disease states or complications. Recognizing that well-designed observational studies can provide valid results is important in the plastic surgery community, so that investigators can both critically appraise and appropriately design observational studies to address important clinical research questions. The investigator planning an observational study can certainly use the STROBE statement as a tool to outline key features of a study and come back to it again at the end to enhance transparency in methodology reporting.


This work was supported in part by a Midcareer Investigator Award in Patient-Oriented Research (K24 AR053120) from the National Institute of Arthritis and Musculoskeletal and Skin Diseases (to K.C.C.).

Enago Academy

Want to Write a Field Study Report? 6 Key Points to Consider!

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Research conduction is not just limited to your laboratory, library, or work place setting. As part of your research you may have to step out in the field (any place other than your regular research lab or work station) to collect raw data for analysis and then publish it as a field study report. In this article, we will discuss the elements of a field study report and the key points to consider while writing one!

Table of Contents

What is a Field Study Report?

A field study report is defined as a documentation of analysis of particular phenomena, behaviors, processes based on theories and observations made by the researcher in the field. These observed and analyzed theories are used to identify solutions for a specific project or case report .

What is the Importance of Field Study Report?

How to Write Field Research Notes?

A field study report begins with an idea and ends with a solution. Hence, while conducting field research, one must follow a planned route of taking notes for proper documentation of the observations made. A successful field study report begins when the researcher is involved in the observational research process of taking proper notes.

Based on the methods, the field research notes are categorized in four different types:

1. Job Notes:

2. Field Notes Proper:

3. Methodological Notes:

4. Journals and Diaries:

Examples of Things to Document During Field Study

1. Physical Setting:

Observe the characteristics of the space where the study is being conducted.

2. Objects and Material:

The presence, placement, and arrangement of objects that affect the behavior of the subject being studied.

3. Language Used:

Observe the language being used by study participants (in case of human participation).

4. Behavior Cycles:

Document who is performing what behavior at what time and situation.

5. Physical Characteristics of Participants/Subjects:

Observe and note personal characteristics of subjects.

6. Body Movements:

Things such as body posture or facial expressions and assess if these movements support or contradict the language used while communicating.

Data Collection in Field Report (Sampling Techniques)

Data collection process in field study is also known as sampling. It refers to the process used to select a portion of the population for study. Selection of an ideal sampling technique is imperative to obtain the richest possible source of information to answer the research questions.

Different Types of Sampling Techniques:

Ad Libitum Sampling

This technique involves observing whatever seems interesting at the moment. It does not follow an organized system of recording the observations.

Behavior Sampling

This sampling technique involves watching the entire group of subjects and recording each occurrence of a specific behavior of interest with reference to which individuals were involved.

Continuous Recording

This sampling technique includes recording of frequencies, durations, and latencies in a continuous and systematic pattern.

Focal Sampling

The focal sampling technique involves observing one individual/subject for a specified amount of time and recording all instances of that individual’s behavior.

Instantaneous Sampling

The technique of instantaneous sampling involves dividing observation sessions into short intervals by sample points.

One-Zero Sampling

The one-zero sampling technique is similar to instantaneous sampling. It involves recording only if the behaviors of interest have occurred at any time during an interval instead of at the instant of the sampling point.

Scan Sampling

The scan sampling technique involves taking a census of the entire observed group at predetermined time periods and recording what each individual is doing at that moment.

What is the Structure and Writing Style of Field Study Report?

A field study report does not have a standard format; however, the following factors determined its structure and writing style:

A field study report includes 6 main elements as follows:

1. Introduction

The introduction section should describe the objective and important theories or concepts underpinning your field study. More importantly, it should describe the organization’s nature or setting where you are conducting the observation—the types of observations conducted, the focus of your research study, what was observed, and which methods were used for collecting the data. Furthermore, it is important to include a review of pertinent literature .

2. Description of Activities

It becomes imperative for researchers to provide the information to the readers about what happened during the field study. Hence, you must include the details of all events that take place during your field research.

The description section helps in answering the five “WH” questions as mentioned below:

What did you see and hear in your area of study?

Where does the background information of the research setting is observed and reported?

Why are you conducting this field research?,

The reason behind particular thing happening , and

Why have you included or excluded specific information?

Who are the participants in terms of gender, age, ethnicity, and other relevant variables from your observation?

When is the study being conducted (day or time when occurring actions are observed and noted)?

3. Analysis and Interpretation

While you are on the field conducting the study, you are likely to observe multiple things. However, it is up to you as to which observations do you want to interpret and record in the report. This allows you to show the reader that you are interpreting events like an informed observer. Furthermore, your theoretical framework helps you in making this decision. The analysis and interpretation of your field observations must always be placed in the larger context of the theories described in the introduction.

Some questions to ask yourself when analyzing your observations are as follows:

4. Conclusion and Recommendations

The conclusion of your field study report should summarize your report and emphasize the importance of your observations. This section has to be concise and relevant to your field study and must not include any new information. Furthermore, it is imperative to highlight any recommendations that you may have for readers to consider while conducting similar study. Additionally, describe any unanticipated problems you encountered and note the limitations of your study. Limit your conclusion to around two to three paragraphs.

5. References

The reference section must include every source that you referred to and used while writing your field study report. Since format for writing references may differ for every university, you must consult your professor to understand the format and write it accordingly.

6. Appendix

This section includes information that is not essential to explain your findings, but supports your analysis [especially repetitive or lengthy information]. It validates your conclusions and contextualize a related point. This helps the reader to understand the overall field study report.

6 Key Points to Consider While Writing a Field Study Report

A field study report focuses on factual and observational details of a project case. It must help the reader understand how theory applies to real-world scenarios. Hence, it should cover the circumstances and contributing factors to derive conclusive results from the observed and collated raw data.

Below are the key points to consider while writing a field study report:

field study report

1. Define the Objective of Your Field Report

2. Construct a Theoretical Framework

3. Record Study Observations and Analysis

4. Include Photo Evidence of Observed Items

5. Record Overall Assessment and Recommendations

6. Validate the Observations with a Signature

Did you every try writing a field study report? How difficult or easy was it? What methods do you follow while writing a field report? Let us know about it in the comments section below!

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very well written….the enumeration is really commendable dear Bhosale…sweet regards from Nepal..

Very well explained and detailed. The information was relevant to my research. thanks

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Study protocol article, a protocol for the use of case reports/studies and case series in systematic reviews for clinical toxicology.

observational study and case reports

Introduction: Systematic reviews are routinely used to synthesize current science and evaluate the evidential strength and quality of resulting recommendations. For specific events, such as rare acute poisonings or preliminary reports of new drugs, we posit that case reports/studies and case series (human subjects research with no control group) may provide important evidence for systematic reviews. Our aim, therefore, is to present a protocol that uses rigorous selection criteria, to distinguish high quality case reports/studies and case series for inclusion in systematic reviews.

Methods: This protocol will adapt the existing Navigation Guide methodology for specific inclusion of case studies. The usual procedure for systematic reviews will be followed. Case reports/studies and case series will be specified in the search strategy and included in separate sections. Data from these sources will be extracted and where possible, quantitatively synthesized. Criteria for integrating cases reports/studies and case series into the overall body of evidence are that these studies will need to be well-documented, scientifically rigorous, and follow ethical practices. The instructions and standards for evaluating risk of bias will be based on the Navigation Guide. The risk of bias, quality of evidence and the strength of recommendations will be assessed by two independent review teams that are blinded to each other.

Conclusion: This is a protocol specified for systematic reviews that use case reports/studies and case series to evaluate the quality of evidence and strength of recommendations in disciplines like clinical toxicology, where case reports/studies are the norm.


Systematic reviews are routinely relied upon to qualitatively synthesize current knowledge in a subject area. These reviews are often paired with a meta-analysis for quantitative syntheses. These qualitative and quantitative summaries of pooled data, collectively evaluate the quality of the evidence and the strength of the resulting research recommendations.

There currently exist several guidance documents to instruct on the rigors of systematic review methodology: (i) the Cochrane Collaboration, Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement and PRISMA-P (for protocols) that offer directives on data synthesis; and (ii) the Grading of Recommendations, Assessment, Development and Evaluations (GRADE) guidelines that establish rules for the development of scientific recommendations ( 1 – 5 ). This systematic review guidance is based predominantly on clinical studies, where randomized controlled trials (RCTs) are the gold standard. For that reason, a separate group of researchers has designed the Navigation Guide, specific to environmental health studies that are often observational ( 6 , 7 ). To date, systematic review guidelines (GRADE, PRISMA, PRISMA-P, and Navigation Guide) remove case reports/studies and case series (human subjects research with no control group) from consideration in systematic reviews, in part due to the challenges in evaluating the internal validity of these kinds of study designs. We hypothesize, however, that under certain circumstances, such as in rare acute poisonings, or preliminary reports of new drugs, some case reports and case series may contribute relevant knowledge that would be informative to systematic review recommendations. This is particularly important in clinical settings, where such evidence could potentially change our understanding of the screening, presentation, and potential treatment of rare conditions, such as poisoning from obscure toxins. The Cochrane Collaboration handbook states that “ for some rare or delayed adverse outcomes only case series or case-control studies may be available. Non-randomized studies of interventions with some study design features that are more susceptible to bias may be acceptable for evaluation of serious adverse events in the absence of better evidence, but the risk of bias must still be assessed and reported ” ( 8 ). In addition, the Cochrane Adverse Effects group has shown that case studies may be the best settings in which to observe adverse effects, especially when they are rare and acute ( 9 ). We believe that there may be an effective way to consider case reports/studies and case series in systematic reviews, specifically by developing specific criteria for their inclusion and accounting for their inherent bias.

We propose here a systematic review protocol that has been specifically developed to consider the inclusion and integration of case reports/studies and case series. Our main objective is to create a protocol that is an adaptation of the Navigation Guide ( 6 , 10 ) that presents methodology to examine high quality case reports/studies and case series through cogent inclusion and exclusion criteria. This methodology is in concordance with the Cochrane Methods for Adverse Effects for scoping reviews ( 11 ).

This protocol was prepared in accordance with the usual structured methodology for systematic reviews (PRISMA, PRISMA-P, and Navigation guide) ( 3 – 7 , 10 ). The protocol will be registered on an appropriate website, such as one of the following:

(i) The International Prospective Register of Systematic Reviews (PROSPERO) database ( https://www.crd.york.ac.uk/PROSPERO/ ) is an international database of prospectively registered systematic reviews in health and social welfare, public health, education, crime, justice, and international development, where there is a health-related outcome. It aims to provide a comprehensive listing of systematic reviews registered at inception to help avoid duplication and reduce opportunity for reporting bias by enabling comparison of the completed review with what was planned in the protocol. PROSPERO accepts registrations for systematic reviews, rapid reviews, and umbrella reviews. Key elements of the review protocol are permanently recorded and stored.

(ii) The Open Science Framework (OSF) platform ( https://osf.io/ ) is a free, open, and integrated platform that facilitates open collaboration in research science. It allows for the management and sharing of research project at all stages of research for broad dissemination. It also enables capture of different aspects and products of the research lifecycle, from the development of a research idea, through the design of a study, the storage and analysis of collected data, to the writing and publication of reports or research articles.

(iii) The Research Registry (RR) database ( https://www.researchregistry.com/ ) is a one-stop repository for the registration of all types of research studies, from “first-in-man” case reports/studies to observational/interventional studies to systematic reviews and meta-analyses. The goal is to ensure that every study involving human participants is registered in accordance with the 2013 Declaration of Helsinki. The RR enables prospective or retrospective registrations of studies, including those types of studies that cannot be registered in existing registries. It specifically publishes systematic reviews and meta-analyses and does not register case reports/studies that are not first-in-man or animal studies.

Any significant future changes to the protocol resulting from knowledge gained during the development stages of this project will be documented in detail and a rationale for all changes will be proposed and reported in PROSPERO, OSF, or RR.

The overall protocol will differentiate itself from other known methodologies, by defining two independent teams of reviewers: a classical team and a case team. The classical team will review studies with control groups and an acceptable comparison group (case reports/studies and case series will be excluded). In effect, this team will conduct a more traditional systematic review where evidence from case reports/studies and case series are not considered. The case team will review classical studies, case reports, and case series. This case team will act as a comparison group to identify differences in systematic review conclusions due to the inclusion of evidence from case reports/studies and case series. Both teams will identify studies that meet specified inclusion criteria, conduct separate analyses and risk of bias evaluations, along with overall quality assessments, and syntheses of strengths of evidence. Each team will be blinded to the results of the other team throughout the process. Upon completion of the systematic review, results from each team will be presented, evaluated, and compared.

Patient and Public Involvement

No patient involved.

Eligibility Criteria

Studies will be selected according to the criteria outlined below.

Study Designs

Studies of any design reported in any translatable language to English by online programs (e.g., Google Translate) will be included at the beginning. These studies will span interventional studies with control groups (Randomized Controlled Trials: RCTs), as well as observational studies with and without exposed groups. All observational studies will be eligible for inclusion in accordance with the objectives of this systematic review. Thereafter, only the case team will include cases reports/studies and case series, as specified in their search strategy. The case team will include a separate section for human subjects research that has been conducted with no control groups.

Type of Population

All types of studies examining the general adult human population or healthy adult humans will be included. Studies that involve both adults and children will also be included if data for adults are reported separately. Animal studies will be excluded for the methodological purpose of this (case reports/studies and case series) protocol given that the framework for systematic reviews in toxicology already adequately retrieves this type of toxin data on animals.

Inclusion/Exclusion Criteria

Studies of any design will be included if they fulfill all the eligibility criteria. To be integrated into the overall body of evidence, cases reports/studies and case series must meet pre-defined criteria indicating that they are well-documented, scientifically rigorous, and follow ethical practices, under the CARE guidelines (for Ca se Re ports) ( 12 , 13 ) and the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Case reports/studies and for Case Series ( 14 , 15 ) that classify case reports/studies in terms of completeness, transparency and data analysis. Studies that were conducted using unethical practices will be excluded.

Type of Exposure/Intervention

Either the prescribed treatment or described exposure to a chemical substance (toxin/toxicant) will be detailed here.

Type of Comparators

In this protocol we plan to compare two review methodologies: one will include and the other will exclude high quality case reports/studies and case series; these two review methodologies will be compared. The comparator will be (the presence or absence of) an available control group that has been specified and is acceptable scientifically and ethically.

Type of Outcomes

The outcome of mortality or morbidity related to the toxicological exposure, will be detailed here.

Information Sources and Search Strategy

There will be no design, date or language limitations applied to the search strategy. A systematic search in electronic academic databases, electronic grey literature, organizational websites, and internet search engines will be performed. We will search at least the following major databases:

- Electronic academic databases : Pubmed, Web of Sciences, Toxline, Poisondex, and databases specific to case reports/studies and case series (e.g., PMC, Scopus, Medline) ( 13 )

- Electronic grey literature databases : OpenGrey ( http://www.opengrey.eu/ ), grey literature Report ( http://greylit.org/ )

- Organizational websites : AHRQ Patient Safety Network ( https://psnet.ahrq.gov/webmm ), World Health Organization ( www.who.int )

- Internet search engines : Google ( https://www.google.com/ ), GoogleScholar ( https://scholar.google.com/ ).

Study Records

Following a systematic search in all the databases above, each of the two independent teams of reviewers (the classical team and the case team) will, respectively, upload separately and in accordance with the eligibility criteria, the literature search results to the systematic review management software, “Covidence,” a primary screening and data extraction tool ( 16 ).

All study records identified during the search will be downloaded and duplicate records will be identified and deleted. Thereafter, two research team members will independently screen the titles and abstracts (step 1) and then the full texts (step 2) of potentially relevant studies for inclusion. If necessary, information will be requested from the publication authors to resolve questions about eligibility. Finally, any disagreements that may potentially exist between the two research team members will be resolved first by discussion and then by consulting a third research team member for arbitration.

If a study record identified during the search was authored by a reviewing research team member, or that team member participated in the identified study, that study record will be re-assigned to another reviewing team member.

Data Collection Process, Items Included, and Prioritization if Needed

All reviewing team members will use standardized forms or software (e.g., Covidence), and each review member will independently extract the data from included studies. If possible, the extracted data will be synthesized numerically. To ensure consistency across reviewers, calibration exercises (reviewer training) will be conducted prior to starting the reviews. Extracted information will include the minimum study characteristics (study authors, study year, study country, participants, intervention/exposure, outcome), study design (summary of study design, comparator, models used, and effect estimate measure) and study context (e.g., data on simultaneous exposure to other risk factors that would be relevant contributors to morbidity or mortality). As specified in the section on study records, a third review team member will resolve any conflicts that arise during data extraction that are not resolved by consensus between the two initial data extractors.

Data on potential conflict of interest for included studies, as well as financial disclosures and funding sources, will also be extracted. If no financial statement or conflict of interest declaration is available, the names of the authors will be searched in other studies published within the previous 36 months and in other publicly available declarations of interests, for funding information ( 17 , 18 ).

Risk of Bias Assessment

To assess the risk of bias within included studies, the internal validity of potential studies will be assessed by using the Navigation Guide tool ( 6 , 19 ), which covers nine domains of bias for human studies: (a) source population representation; (b) blinding; (c) exposure or intervention assessment; (d) outcome assessment; (e) confounding; (f) incomplete outcome data; (g) selective outcome reporting; (h) conflict of interest; and (i) other sources of bias. For each section of the tool, the procedures undertaken for each study will be described and the risk of bias will be rated as “ low risk”; “probably low risk”; “probably risk”; “high risk”; or “not applicable.” Risk of bias on the levels of the individual study and the entire body of evidence will be assessed. Most of the text from these instructions and criteria for judging risk of bias has been adopted verbatim or adapted from one of the latest Navigation Guide systematic reviews used by WHO/ILO ( 6 , 19 , 20 ).

For case reports/studies and case series, the text from these instructions and criteria for judging risk of bias has been adopted verbatim or adapted from one of the latest Navigation Guide systematic reviews ( 21 ), and is given in Supplementary Material . Specific criteria are listed below. To ensure consistency across reviewers, calibration exercises (reviewer training) will be conducted prior to starting the risk of bias assessments for case reports/studies and case series.

Are the Study Groups at Risk of Not Representing Their Source Populations in a Manner That Might Introduce Selection Bias?

The source population is viewed as the population for which study investigators are targeting their study question of interest.

Examples of considerations for this risk of bias domain include: (1) the context of the case report; (2) level of detail reported for participant inclusion/exclusion (including details from previously published papers referenced in the article), with inclusion of all relevant consecutive patients in the considered period; ( 14 , 15 ) (3) exclusion rates, attrition rates and reasons.

Were Exposure/Intervention (Toxic, Treatment) Assessment Methods Lacking Accuracy?

The following list of considerations represents a collection of factors proposed by experts in various fields that may potentially influence the internal validity of the exposure assessment in a systematic manner (not those that may randomly affect overall study results). These should be interpreted only as suggested considerations and should not be viewed as scoring or a checklist . Considering there are no controls in such designs, this should be evaluated carefully to be sure the report really contributes to the actual knowledge .

List of Considerations :

Possible sources of exposure assessment metrics:

1) Identification of the exposure

2) Dose evaluation

3) Toxicological values

4) Clinical effects *

5) Biological effects *

6) Treatments given (dose, timing, route)

* Some clinical and biological effects might be related to exposure

For each, overall considerations include:

1) What is the quality of the source of the metric being used?

2) Is the exposure measured in the study a surrogate for the exposure?

3) What was the temporal coverage (i.e., short or long-term exposure)?

4) Did the analysis account for prediction uncertainty?

5) How was missing data accounted for, and any data imputations incorporated?

6) Were sensitivity analyses performed?

Were Outcome Assessment Methods Lacking Accuracy?

This item is similar to actual Navigation guidelines that require an assessment of the accuracy of the measured outcome.

Was Potential Confounding Inadequately Incorporated?

This is a very important issue for case reports/studies and case series. Case reports/studies and case series do not include controls and so, to be considered in a systematic review, these types of studies will need to be well-documented with respect to treatment or other contextual factors that may explain or influence the outcome. Prior to initiating the study screening, review team members should collectively generate a list of potential confounders that are based on expert opinion and knowledge gathered from the scientific literature:

Tier I: Important confounders

• Other associated treatment (i.e., intoxication, insufficient dose, history, or context)

• Medical history

Tier II: Other potentially important confounders and effect modifiers:

• Age, sex, country.

Were Incomplete Outcome Data Inadequately Addressed?

This item is similar to actual Navigation Guide instructions, though it may be very unlikely that outcome data would be incomplete in published case reports/studies and case series.

Does the Study Report Appear to Have Selective Outcome Reporting?

This item is similar to actual Navigation Guide instructions, though it may be very unlikely that there would be selective outcome reporting in published case reports/studies and case series.

Did the Study Receive Any Support From a Company, Study Author, or Other Entity Having a Financial Interest?

This item is similar to actual Navigation Guide instructions.

Did the Study Appear to Have Other Problems That Could Put It at a Risk of Bias?

Data synthesis criteria and summary measures if feasible.

Meta-analyses will be conducted using a random-effects model if studies are sufficiently homogeneous in terms of design and comparator. For dichotomous outcomes, effects of associations will be determined by using risk ratios (RR) or odds ratios (OR) with 95% confidence intervals (CI). Continuous outcomes will be analyzed using weighted mean differences (with 95% CI) or standardized mean differences (with 95% CI) if different measurement scales are used. Skewed data and non-quantitative data will be presented descriptively. Where data are missing, a request will be made to the original authors of the study to obtain the relevant missing data. If these data cannot be obtained, an imputation method will be performed. The statistical heterogeneity of the studies using the Chi Squared test (significance level: 0.1) and I 2 statistic (0–40%: might not be important; 30–60%: may represent moderate heterogeneity; 50–90%: may represent substantial heterogeneity; 75–100%: considerable heterogeneity). If there is heterogeneity, an attempt will be made to explain the source of this heterogeneity through a subgroup or sensitivity analysis.

Finally, the meta-analysis will be conducted in the latest version of the statistical software RevMan. The Mantel-Haenszel method will be used for the fixed effects model if tests of heterogeneity are not significant. If statistical heterogeneity is observed ( I 2 ≥ 50% or p < 0.1), the random effects model will be chosen. If quantitative synthesis is not feasible (e.g., if heterogeneity exists), a meta-analysis will not be performed and a narrative, qualitative summary of the study findings will be done.

Separate analyses will be conducted for the studies that contain control groups using expected mortality/morbidity, in order to include them in the quantitative synthesis of case reports/studies and case series.

If quantitative synthesis is not appropriate, a systematic narrative synthesis will be provided with information presented in the text and tables to summarize and explain the characteristics and findings of the included studies. The narrative synthesis will explore the relationship and findings both within and between the included studies.

Possible Additional Analyses

If feasible, subgroup analyses will be used to explore possible sources of heterogeneity, if there is evidence for differences in effect estimates by country, study design, or patient characteristics (e.g., sex and age). In addition, sensitivity analysis will be performed to explore the source of heterogeneity as for example, published vs. unpublished data, full-text publications vs. abstracts, risk of bias (by omitting studies that are judged to be at high risk of bias).

Overall Quality of Evidence Assessment

The quality of evidence will be assessed using an adapted version of the Evidence Quality Assessment Tool in the Navigation Guide. This tool is based on the GRADE approach ( 1 ). The assessment will be conducted by two teams, again blinded to each other, one that has the results of the case reports/studies and case series/control synthesis, the other without.

Data synthesis will be conducted independently by the classical and case teams. Evidence ratings will start at “high” for randomized control studies, “moderate” for observational studies, and “low” for case reports/studies and case series . It is important to be clear that sufficient levels of evidence cannot be achieved without study comparators. With regards to case reports/studies and case series, we classify these as starting at the lowest point of evidence and therefore we cannot consider evidence higher than low for these kinds of studies. Complete instructions for making quality of evidence judgments are presented in Supplementary Material .

Synthesis of Strength of Evidence

The standard Navigation Guide methodology will be applied to rate the strength of recommendations. The classical and case teams, blinded to the results from each other during the process, will independently assess the strength of evidence. The evidence quality ratings will be translated into strength of evidence for each population based on a combination of four criteria: (a) Quality of body of evidence; (b) Direction of effect; (c) Confidence in effect; and (d) Other compelling attributes of the data that may influence certainty. The ratings for strength of evidence will be “sufficient evidence of harmfulness,” “limited of harmfulness,” “inadequate of harmfulness” and “evidence of lack of harmfulness.”

Once we complete the synthesis of case reports/studies and case series, findings of this separate evidence stream will only be considered if RCTs and observational studies are not available. They will not be used to upgrade or downgrade the strength of other evidence streams.

To the best of our knowledge, this protocol is one of the first to specifically address the incorporation of case reports/studies and case series in a systematic review ( 9 ). The protocol was adapted from the Navigation Guide with the intent of integrating the case reports/studies and case series in systematic review recommendations, while following traditional systematic review methodology to the greatest extent possible. To be included, these case report/studies and case series will need to be well-documented, scientifically rigorous, and follow ethical practices. In addition, we believe that some case reports/studies and case series might bring relevant knowledge that should be considered in systematic review recommendations when data from RCT's and observational studies are not available, especially when even a small number of studies report an important and possibly causal association in an epidemic or a side effect of a newly marketed medicine. Our methodology will be the first to effectively incorporate case reports/studies and case series in systematic reviews that synthesize evidence for clinicians, researchers, and drug developers. These types of studies will be incorporated mostly through paper selection and risk of bias assessments. In addition, we will conduct meta-analyses if the eligible studies provide sufficient data.

This protocol has limitations related primarily to the constraints of case reports/studies and case series. These are descriptive studies. In addition, a case series is subject to selection bias because the clinician or researcher selects the cases themselves and may represent outliers in clinical practice. Furthermore, this kind of study does not have a control group, so it is not possible to compare what happens to other people who do not have the disease or receive treatment. These sources of bias mean that reported results may not be generalizable to a larger patient population and therefore cannot generate information on incidences or prevalence rates and ratios ( 22 , 23 ). However, it is important to note that promoting the need to synthesize these types of studies (case reports/studies and case series) in a formal systematic review, should not deter or delay immediate action from being taken when a few small studies report a plausible causal association between exposure and disease, such as, in the event of an epidemic or a side effect of a newly marketed medicine ( 23 ). In this study protocol, we will not consider animal studies that might give relevant toxicological information because we are focusing on study areas where a paucity of information exists. Finally, we must note that, case reports/studies and case series do not provide independent proof, and therefore, the findings of this separate evidence stream (case reports/studies and case series) will only be considered if evidence from RCTs and observational studies is not available. Case reports/studies and case series will not be used to upgrade or downgrade the strength of other evidence streams. In any case, it is very important to remember that these kinds of studies (case reports/studies and case series) are there to quickly alert agencies of the need to take immediate action to prevent further harm.

Despite these limitations, case reports/studies and case series are a first line of evidence because they are where new issues and ideas emerge (hypothesis-generating) and can contribute to a change in clinical practice ( 23 – 25 ). We therefore believe that data from case reports/studies and case series, when synthesized and presented with completeness and transparency, may provide important details that are relevant to systematic review recommendations.

Author Contributions

AD and GS the protocol study was designed. JL, TW, and DM reviewed. MF, ALG, RV, NC, CB, GLR, MD, ML, and AN significant improvement was made. AN and AD wrote the manuscript. GS improved the language. All authors reviewed and commented on the final manuscript, read and approved the final manuscript to be published.

This project was supported by the French Pays de la Loire region and Angers Loire Métropole, University of Angers and Centre Hospitalo-Universitaire CHU Angers. The project is entitled TEC-TOP (no award/grant number).

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fmed.2021.708380/full#supplementary-material

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Keywords: toxicology, epidemiology, public health, protocol, systematic review, case reports/studies, case series

Citation: Nambiema A, Sembajwe G, Lam J, Woodruff T, Mandrioli D, Chartres N, Fadel M, Le Guillou A, Valter R, Deguigne M, Legeay M, Bruneau C, Le Roux G and Descatha A (2021) A Protocol for the Use of Case Reports/Studies and Case Series in Systematic Reviews for Clinical Toxicology. Front. Med. 8:708380. doi: 10.3389/fmed.2021.708380

Received: 19 May 2021; Accepted: 11 August 2021; Published: 06 September 2021.

Reviewed by:

Copyright © 2021 Nambiema, Sembajwe, Lam, Woodruff, Mandrioli, Chartres, Fadel, Le Guillou, Valter, Deguigne, Legeay, Bruneau, Le Roux and Descatha. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Aboubakari Nambiema, aboubakari.nambiema@univ-angers.fr ; orcid.org/0000-0002-4258-3764

Case Report vs Cross-Sectional Study: A Simple Explanation

A case report is the description of the clinical story of a single patient. A cross-sectional study involves a group of participants on which data is collected at a single point in time to investigate the relationship between a certain exposure and an outcome.

Here’s a table that summarizes the relationship between a case report and a cross-sectional study:

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Observational descriptive study: case report, case series & ecological study

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6.5 Observational Research

Learning objectives.

What Is Observational Research?

The term observational research is used to refer to several different types of non-experimental studies in which behavior is systematically observed and recorded. The goal of observational research is to describe a variable or set of variables. More generally, the goal is to obtain a snapshot of specific characteristics of an individual, group, or setting. As described previously, observational research is non-experimental because nothing is manipulated or controlled, and as such we cannot arrive at causal conclusions using this approach. The data that are collected in observational research studies are often qualitative in nature but they may also be quantitative or both (mixed-methods). There are several different types of observational research designs that will be described below.

Naturalistic Observation

Naturalistic observation  is an observational method that involves observing people’s behavior in the environment in which it typically occurs. Thus naturalistic observation is a type of field research (as opposed to a type of laboratory research). Jane Goodall’s famous research on chimpanzees is a classic example of naturalistic observation. Dr.  Goodall spent three decades observing chimpanzees in their natural environment in East Africa. She examined such things as chimpanzee’s social structure, mating patterns, gender roles, family structure, and care of offspring by observing them in the wild. However, naturalistic observation  could more simply involve observing shoppers in a grocery store, children on a school playground, or psychiatric inpatients in their wards. Researchers engaged in naturalistic observation usually make their observations as unobtrusively as possible so that participants are not aware that they are being studied. Such an approach is called disguised naturalistic observation.  Ethically, this method is considered to be acceptable if the participants remain anonymous and the behavior occurs in a public setting where people would not normally have an expectation of privacy. Grocery shoppers putting items into their shopping carts, for example, are engaged in public behavior that is easily observable by store employees and other shoppers. For this reason, most researchers would consider it ethically acceptable to observe them for a study. On the other hand, one of the arguments against the ethicality of the naturalistic observation of “bathroom behavior” discussed earlier in the book is that people have a reasonable expectation of privacy even in a public restroom and that this expectation was violated. 

In cases where it is not ethical or practical to conduct disguised naturalistic observation, researchers can conduct  undisguised naturalistic observation where the participants are made aware of the researcher presence and monitoring of their behavior. However, one concern with undisguised naturalistic observation is  reactivity. Reactivity  refers to when a measure changes participants’ behavior. In the case of undisguised naturalistic observation, the concern with reactivity is that when people know they are being observed and studied, they may act differently than they normally would. For instance, you may act much differently in a bar if you know that someone is observing you and recording your behaviors and this would invalidate the study. So disguised observation is less reactive and therefore can have higher validity because people are not aware that their behaviors are being observed and recorded. However, we now know that people often become used to being observed and with time they begin to behave naturally in the researcher’s presence. In other words, over time people habituate to being observed. Think about reality shows like Big Brother or Survivor where people are constantly being observed and recorded. While they may be on their best behavior at first, in a fairly short amount of time they are, flirting, having sex, wearing next to nothing, screaming at each other, and at times acting like complete fools in front of the entire nation.

Participant Observation

Another approach to data collection in observational research is participant observation. In  participant observation , researchers become active participants in the group or situation they are studying. Participant observation is very similar to naturalistic observation in that it involves observing people’s behavior in the environment in which it typically occurs. As with naturalistic observation, the data that is collected can include interviews (usually unstructured), notes based on their observations and interactions, documents, photographs, and other artifacts. The only difference between naturalistic observation and participant observation is that researchers engaged in participant observation become active members of the group or situations they are studying. The basic rationale for participant observation is that there may be important information that is only accessible to, or can be interpreted only by, someone who is an active participant in the group or situation. Like naturalistic observation, participant observation can be either disguised or undisguised. In disguised participant observation, the researchers pretend to be members of the social group they are observing and conceal their true identity as researchers. In contrast with undisguised participant observation,  the researchers become a part of the group they are studying and they disclose their true identity as researchers to the group under investigation. Once again there are important ethical issues to consider with disguised participant observation.  First no informed consent can be obtained and second passive deception is being used. The researcher is passively deceiving the participants by intentionally withholding information about their motivations for being a part of the social group they are studying. But sometimes disguised participation is the only way to access a protective group (like a cult). Further,  disguised participant observation is less prone to reactivity than undisguised participant observation. 

Rosenhan’s study (1973) [1]   of the experience of people in a psychiatric ward would be considered disguised participant observation because Rosenhan and his pseudopatients were admitted into psychiatric hospitals on the pretense of being patients so that they could observe the way that psychiatric patients are treated by staff. The staff and other patients were unaware of their true identities as researchers.

Another example of participant observation comes from a study by sociologist Amy Wilkins (published in  Social Psychology Quarterly ) on a university-based religious organization that emphasized how happy its members were (Wilkins, 2008) [2] . Wilkins spent 12 months attending and participating in the group’s meetings and social events, and she interviewed several group members. In her study, Wilkins identified several ways in which the group “enforced” happiness—for example, by continually talking about happiness, discouraging the expression of negative emotions, and using happiness as a way to distinguish themselves from other groups.

One of the primary benefits of participant observation is that the researcher is in a much better position to understand the viewpoint and experiences of the people they are studying when they are apart of the social group. The primary limitation with this approach is that the mere presence of the observer could affect the behavior of the people being observed. While this is also a concern with naturalistic observation when researchers because active members of the social group they are studying, additional concerns arise that they may change the social dynamics and/or influence the behavior of the people they are studying. Similarly, if the researcher acts as a participant observer there can be concerns with biases resulting from developing relationships with the participants. Concretely, the researcher may become less objective resulting in more experimenter bias.

Structured Observation

Another observational method is structured observation. Here the investigator makes careful observations of one or more specific behaviors in a particular setting that is more structured than the settings used in naturalistic and participant observation. Often the setting in which the observations are made is not the natural setting, rather the researcher may observe people in the laboratory environment. Alternatively, the researcher may observe people in a natural setting (like a classroom setting) that they have structured some way, for instance by introducing some specific task participants are to engage in or by introducing a specific social situation or manipulation. Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Structured observation is very similar to naturalistic observation and participant observation in that in all cases researchers are observing naturally occurring behavior, however, the emphasis in structured observation is on gathering quantitative rather than qualitative data. Researchers using this approach are interested in a limited set of behaviors. This allows them to quantify the behaviors they are observing. In other words, structured observation is less global than naturalistic and participant observation because the researcher engaged in structured observations is interested in a small number of specific behaviors. Therefore, rather than recording everything that happens, the researcher only focuses on very specific behaviors of interest.

Researchers Robert Levine and Ara Norenzayan used structured observation to study differences in the “pace of life” across countries (Levine & Norenzayan, 1999) [3] . One of their measures involved observing pedestrians in a large city to see how long it took them to walk 60 feet. They found that people in some countries walked reliably faster than people in other countries. For example, people in Canada and Sweden covered 60 feet in just under 13 seconds on average, while people in Brazil and Romania took close to 17 seconds. When structured observation  takes place in the complex and even chaotic “real world,” the questions of when, where, and under what conditions the observations will be made, and who exactly will be observed are important to consider. Levine and Norenzayan described their sampling process as follows:

“Male and female walking speed over a distance of 60 feet was measured in at least two locations in main downtown areas in each city. Measurements were taken during main business hours on clear summer days. All locations were flat, unobstructed, had broad sidewalks, and were sufficiently uncrowded to allow pedestrians to move at potentially maximum speeds. To control for the effects of socializing, only pedestrians walking alone were used. Children, individuals with obvious physical handicaps, and window-shoppers were not timed. Thirty-five men and 35 women were timed in most cities.” (p. 186).  Precise specification of the sampling process in this way makes data collection manageable for the observers, and it also provides some control over important extraneous variables. For example, by making their observations on clear summer days in all countries, Levine and Norenzayan controlled for effects of the weather on people’s walking speeds.  In Levine and Norenzayan’s study, measurement was relatively straightforward. They simply measured out a 60-foot distance along a city sidewalk and then used a stopwatch to time participants as they walked over that distance.

As another example, researchers Robert Kraut and Robert Johnston wanted to study bowlers’ reactions to their shots, both when they were facing the pins and then when they turned toward their companions (Kraut & Johnston, 1979) [4] . But what “reactions” should they observe? Based on previous research and their own pilot testing, Kraut and Johnston created a list of reactions that included “closed smile,” “open smile,” “laugh,” “neutral face,” “look down,” “look away,” and “face cover” (covering one’s face with one’s hands). The observers committed this list to memory and then practiced by coding the reactions of bowlers who had been videotaped. During the actual study, the observers spoke into an audio recorder, describing the reactions they observed. Among the most interesting results of this study was that bowlers rarely smiled while they still faced the pins. They were much more likely to smile after they turned toward their companions, suggesting that smiling is not purely an expression of happiness but also a form of social communication.

When the observations require a judgment on the part of the observers—as in Kraut and Johnston’s study—this process is often described as  coding . Coding generally requires clearly defining a set of target behaviors. The observers then categorize participants individually in terms of which behavior they have engaged in and the number of times they engaged in each behavior. The observers might even record the duration of each behavior. The target behaviors must be defined in such a way that different observers code them in the same way. This difficulty with coding is the issue of interrater reliability, as mentioned in Chapter 4. Researchers are expected to demonstrate the interrater reliability of their coding procedure by having multiple raters code the same behaviors independently and then showing that the different observers are in close agreement. Kraut and Johnston, for example, video recorded a subset of their participants’ reactions and had two observers independently code them. The two observers showed that they agreed on the reactions that were exhibited 97% of the time, indicating good interrater reliability.

One of the primary benefits of structured observation is that it is far more efficient than naturalistic and participant observation. Since the researchers are focused on specific behaviors this reduces time and expense. Also, often times the environment is structured to encourage the behaviors of interested which again means that researchers do not have to invest as much time in waiting for the behaviors of interest to naturally occur. Finally, researchers using this approach can clearly exert greater control over the environment. However, when researchers exert more control over the environment it may make the environment less natural which decreases external validity. It is less clear for instance whether structured observations made in a laboratory environment will generalize to a real world environment. Furthermore, since researchers engaged in structured observation are often not disguised there may be more concerns with reactivity.

Case Studies

A  case study  is an in-depth examination of an individual. Sometimes case studies are also completed on social units (e.g., a cult) and events (e.g., a natural disaster). Most commonly in psychology, however, case studies provide a detailed description and analysis of an individual. Often the individual has a rare or unusual condition or disorder or has damage to a specific region of the brain.

Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting. If the natural setting is not what is of interest, then the individual may be brought into a therapist’s office or a researcher’s lab for study. Also, the bulk of the case study report will focus on in-depth descriptions of the person rather than on statistical analyses. With that said some quantitative data may also be included in the write-up of a case study. For instance, an individuals’ depression score may be compared to normative scores or their score before and after treatment may be compared. As with other qualitative methods, a variety of different methods and tools can be used to collect information on the case. For instance, interviews, naturalistic observation, structured observation, psychological testing (e.g., IQ test), and/or physiological measurements (e.g., brain scans) may be used to collect information on the individual.

HM is one of the most notorious case studies in psychology. HM suffered from intractable and very severe epilepsy. A surgeon localized HM’s epilepsy to his medial temporal lobe and in 1953 he removed large sections of his hippocampus in an attempt to stop the seizures. The treatment was a success, in that it resolved his epilepsy and his IQ and personality were unaffected. However, the doctors soon realized that HM exhibited a strange form of amnesia, called anterograde amnesia. HM was able to carry out a conversation and he could remember short strings of letters, digits, and words. Basically, his short term memory was preserved. However, HM could not commit new events to memory. He lost the ability to transfer information from his short-term memory to his long term memory, something memory researchers call consolidation. So while he could carry on a conversation with someone, he would completely forget the conversation after it ended. This was an extremely important case study for memory researchers because it suggested that there’s a dissociation between short-term memory and long-term memory, it suggested that these were two different abilities sub-served by different areas of the brain. It also suggested that the temporal lobes are particularly important for consolidating new information (i.e., for transferring information from short-term memory to long-term memory).


The history of psychology is filled with influential cases studies, such as Sigmund Freud’s description of “Anna O.” (see Note 6.1 “The Case of “Anna O.””) and John Watson and Rosalie Rayner’s description of Little Albert (Watson & Rayner, 1920) [5] , who learned to fear a white rat—along with other furry objects—when the researchers made a loud noise while he was playing with the rat.

The Case of “Anna O.”

Sigmund Freud used the case of a young woman he called “Anna O.” to illustrate many principles of his theory of psychoanalysis (Freud, 1961) [6] . (Her real name was Bertha Pappenheim, and she was an early feminist who went on to make important contributions to the field of social work.) Anna had come to Freud’s colleague Josef Breuer around 1880 with a variety of odd physical and psychological symptoms. One of them was that for several weeks she was unable to drink any fluids. According to Freud,

She would take up the glass of water that she longed for, but as soon as it touched her lips she would push it away like someone suffering from hydrophobia.…She lived only on fruit, such as melons, etc., so as to lessen her tormenting thirst. (p. 9)

But according to Freud, a breakthrough came one day while Anna was under hypnosis.

[S]he grumbled about her English “lady-companion,” whom she did not care for, and went on to describe, with every sign of disgust, how she had once gone into this lady’s room and how her little dog—horrid creature!—had drunk out of a glass there. The patient had said nothing, as she had wanted to be polite. After giving further energetic expression to the anger she had held back, she asked for something to drink, drank a large quantity of water without any difficulty, and awoke from her hypnosis with the glass at her lips; and thereupon the disturbance vanished, never to return. (p.9)

Freud’s interpretation was that Anna had repressed the memory of this incident along with the emotion that it triggered and that this was what had caused her inability to drink. Furthermore, her recollection of the incident, along with her expression of the emotion she had repressed, caused the symptom to go away.

As an illustration of Freud’s theory, the case study of Anna O. is quite effective. As evidence for the theory, however, it is essentially worthless. The description provides no way of knowing whether Anna had really repressed the memory of the dog drinking from the glass, whether this repression had caused her inability to drink, or whether recalling this “trauma” relieved the symptom. It is also unclear from this case study how typical or atypical Anna’s experience was.

Figure 10.1 Anna O. “Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis. Source: http://en.wikipedia.org/wiki/File:Pappenheim_1882.jpg

Figure 10.1 Anna O. “Anna O.” was the subject of a famous case study used by Freud to illustrate the principles of psychoanalysis. Source: http://en.wikipedia.org/wiki/File:Pappenheim_1882.jpg

Case studies are useful because they provide a level of detailed analysis not found in many other research methods and greater insights may be gained from this more detailed analysis. As a result of the case study, the researcher may gain a sharpened understanding of what might become important to look at more extensively in future more controlled research. Case studies are also often the only way to study rare conditions because it may be impossible to find a large enough sample to individuals with the condition to use quantitative methods. Although at first glance a case study of a rare individual might seem to tell us little about ourselves, they often do provide insights into normal behavior. The case of HM provided important insights into the role of the hippocampus in memory consolidation. However, it is important to note that while case studies can provide insights into certain areas and variables to study, and can be useful in helping develop theories, they should never be used as evidence for theories. In other words, case studies can be used as inspiration to formulate theories and hypotheses, but those hypotheses and theories then need to be formally tested using more rigorous quantitative methods.

The reason case studies shouldn’t be used to provide support for theories is that they suffer from problems with internal and external validity. Case studies lack the proper controls that true experiments contain. As such they suffer from problems with internal validity, so they cannot be used to determine causation. For instance, during HM’s surgery, the surgeon may have accidentally lesioned another area of HM’s brain (indeed questioning into the possibility of a separate brain lesion began after HM’s death and dissection of his brain) and that lesion may have contributed to his inability to consolidate new information. The fact is, with case studies we cannot rule out these sorts of alternative explanations. So as with all observational methods case studies do not permit determination of causation. In addition, because case studies are often of a single individual, and typically a very abnormal individual, researchers cannot generalize their conclusions to other individuals. Recall that with most research designs there is a trade-off between internal and external validity, with case studies, however, there are problems with both internal validity and external validity. So there are limits both to the ability to determine causation and to generalize the results. A final limitation of case studies is that ample opportunity exists for the theoretical biases of the researcher to color or bias the case description. Indeed, there have been accusations that the woman who studied HM destroyed a lot of her data that were not published and she has been called into question for destroying contradictory data that didn’t support her theory about how memories are consolidated. There is a fascinating New York Times article that describes some of the controversies that ensued after HM’s death and analysis of his brain that can be found at: https://www.nytimes.com/2016/08/07/magazine/the-brain-that-couldnt-remember.html?_r=0

Archival Research

Another approach that is often considered observational research is the use of  archival research  which involves analyzing data that have already been collected for some other purpose. An example is a study by Brett Pelham and his colleagues on “implicit egotism”—the tendency for people to prefer people, places, and things that are similar to themselves (Pelham, Carvallo, & Jones, 2005) [7] . In one study, they examined Social Security records to show that women with the names Virginia, Georgia, Louise, and Florence were especially likely to have moved to the states of Virginia, Georgia, Louisiana, and Florida, respectively.

As with naturalistic observation, measurement can be more or less straightforward when working with archival data. For example, counting the number of people named Virginia who live in various states based on Social Security records is relatively straightforward. But consider a study by Christopher Peterson and his colleagues on the relationship between optimism and health using data that had been collected many years before for a study on adult development (Peterson, Seligman, & Vaillant, 1988) [8] . In the 1940s, healthy male college students had completed an open-ended questionnaire about difficult wartime experiences. In the late 1980s, Peterson and his colleagues reviewed the men’s questionnaire responses to obtain a measure of explanatory style—their habitual ways of explaining bad events that happen to them. More pessimistic people tend to blame themselves and expect long-term negative consequences that affect many aspects of their lives, while more optimistic people tend to blame outside forces and expect limited negative consequences. To obtain a measure of explanatory style for each participant, the researchers used a procedure in which all negative events mentioned in the questionnaire responses, and any causal explanations for them were identified and written on index cards. These were given to a separate group of raters who rated each explanation in terms of three separate dimensions of optimism-pessimism. These ratings were then averaged to produce an explanatory style score for each participant. The researchers then assessed the statistical relationship between the men’s explanatory style as undergraduate students and archival measures of their health at approximately 60 years of age. The primary result was that the more optimistic the men were as undergraduate students, the healthier they were as older men. Pearson’s  r  was +.25.

This method is an example of  content analysis —a family of systematic approaches to measurement using complex archival data. Just as structured observation requires specifying the behaviors of interest and then noting them as they occur, content analysis requires specifying keywords, phrases, or ideas and then finding all occurrences of them in the data. These occurrences can then be counted, timed (e.g., the amount of time devoted to entertainment topics on the nightly news show), or analyzed in a variety of other ways.

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"participants are measured or categorized on the basis of the independent variable and are monitored over time to observe occurrence of the dependent variable. In a cohort study, it is established at the outset that subjects have not already exhibited the outcomes of interest (dependent variable)" (Meininger, 2017).

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"participants are observed only once, offering a 'snapshot' of the characteristics of interest at that particular moment" (Research Methods, 2008).

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Meininger, J. C. (2017). Observational research designs. In J. Fitzpatrick (Ed.), Encyclopedia of nursing research (4th ed.). Springer Publishing Company. 

Research methods and measurement and: Cross-sectional. (2008). In I. P. Albery, & M. Munafo, Key concepts in health psychology . Sage UK. 

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Safety and tolerability of nabiximols oromucosal spray: a review of real-world experience in observational studies, registries, and case reports


Introduction: : Nabiximols oromucosal spray,a cannabis-based medicine containing a balanced ratio of Δ-9-tetrahydrocannabinol and cannabidiol, is approved widely as an add-on therapy for symptomatic relief of spasticity in people with multiple sclerosis (MS). Most safety data for nabiximols derive from use in MS spasticity, with some data available from the analgesia area.

Areas covered: : This review compiles safety and tolerability data from all published observational studies, registry analyses, and case reports identified in systematic searches in which nabiximols oromucosal spray was investigated for spasticity (n = 20) and/or chronic non-cancer pain (n = 4). Aligning with the known safety profile of nabiximols as demonstrated in randomized controlled trials, common adverse events reported consistently across studies conducted under clinical practice conditions were dizziness, fatigue and somnolence. The serious adverse event (SAE) rate with nabiximols in MS spasticityobservational studies was 3.1% (137/4351). A total of 39 treatment-related SAEs were reported in 32 patients with spasticity, all of which (where specified) were resolved. No treatment-related SAEs were recorded in nabiximols pain studies.

Expert opinion: : Real-world experience with nabiximols oromucosal spray in treating spasticity and chronic pain indicates that, overall, it is well tolerated and has a good safety profile.

Keywords: Nabiximols; multiple sclerosis spasticity; neurological pain; observational studies; safety; sativex; tolerability.

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observational study and case reports

Short-term outcome after ischemic stroke and 24-h blood pressure variability: association and predictors

17 August 2020

Maria Kamieniarz-Mędrygał, Tomasz Łukomski & Radosław Kaźmierski

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Increased blood pressure variability during the subacute phase of ischemic stroke is associated with poor functional outcomes at 3 months

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Blood pressure fluctuation pattern and stroke outcomes in acute ischemic stroke

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Blood pressure variability and outcome in acute ischemic and hemorrhagic stroke: a post hoc analysis of the HeadPoST study

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Time-variant associations of ambulatory pulse rate with short-term neurologic functional outcomes following acute ischemic stroke

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Association between blood pressure variability and the short-term outcome in patients with acute spontaneous subarachnoid hemorrhage

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Optimal blood pressure after reperfusion therapy in patients with acute ischemic stroke

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Clinical impact of blood pressure on cardiovascular death in patients 80 years and older following acute myocardial infarction: a prospective cohort study

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Significance of pulse pressure variability in predicting functional outcome in acute ischemic stroke: a retrospective, single-center, observational cohort study

Scientific Reports volume  13 , Article number:  3618 ( 2023 ) Cite this article

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Metrics details

This study aimed to determine the association between pulse pressure variability (PPV) and short- and long-term outcomes of acute ischemic stroke (AIS) patients. We studied 203 tertiary stroke center patients with AIS. PPV during 72 h after admission was analyzed using different variability parameters including standard deviation (SD). Patients’ outcome was assessed after 30 and 90 days post-stroke with modified Rankin Scale. The association between PPV and outcome was investigated using logistic regression analysis with adjustment for potential confounders. The predictive significance of PPV parameters was determined using area under the curve (AUC) of receiver operating characteristics. In the unadjusted logistic regression analysis, all PPV indicators were independently associated with unfavorable outcome at 30 days (i.a. Odds ratio (OR) = 4.817, 95%CI 2.283–10.162 per 10 mmHg increase in SD, p = 0.000) and 90 days (i.a. OR = 4.248, 95%CI 2.044–8.831 per 10 mmHg increase in SD, p = 0.000). After adjustment for confounders, ORs for all PPV indicators remained statistically significant. On the basis of AUC values, all PPV parameters were found relevant outcome predictors (p < 0.01). In conclusion, elevated PPV during first 72 h after admission due to AIS is associated with unfavorable outcome at 30 and 90 days, independent of mean blood pressure levels.


Acute hypertensive response occurs frequently in patients during ischemic stroke, but the pathophysiology of that phenomenon remains unknown 1 . Furthermore, it is not clear how to properly control blood pressure (BP) in hyperacute ischemic stroke 2 .Current guidelines suggest setting only the upper threshold of BP 3 . This threshold is different, depending whether the patient is undergoing reperfusion therapy such as intravenous thrombolysis (IVT) or mechanical thrombectomy (MT) or not undergoing such treatment 3 . Therapeutical intervention for BP control is only recommended if the upper BP threshold is exceeded, or in case some specific comorbidity (e.g. acute heart failure, acute coronary event, aorta dissection, preeclampsia) makes it necessary 4 . Current guidelines lack recommendations regarding the lower BP threshold as well as acceptable BP fluctuations in the acute period of stroke. In absence of randomized trials, recommendations are based on observational and retrospective studies and, as a consequence, their strength is weak 5 , 6 . Recent studies have not shown any benefit of aggressive BP reduction in hyperacute period 7 , 8 . By contrast, studies have proved the “U” shaped relationship between BP levels and worse patients outcome and death 9 , 10 .

Beside absolute BP levels, also blood pressure variability (BPV) turned out to be associated with unfavorable outcome 11 , 12 . BPV might be responsible for hypoperfusion and hyperperfusion of vulnerable ischemic penumbra, resulting in either enlargement of the ischemic area and cerebral oedema or hemorrhagic transformation. Currently, there is no unified research methodology for measuring BPV, precluding meta-analysis 13 , 14 . Most studies estimate BP fluctuations by systolic blood pressure (SBP) and mean arterial pressure (MAP) 15 . Diastolic blood pressure (DBP) is less frequently used, and only a few studies employed pulse pressure (PP). Independently of which BP component was the focus, studies up to date have typically used only a single BPV statistic parameter, usually standard deviation (SD), and only few publications took into account several different parameters 15 .

Elevated PP is associated with poor cardiovascular prognosis 16 . It was also demonstrated to be associated with post-stroke mortality and stroke recurrence 17 , 18 , 19 . Pulse pressure is a pulsatile and dynamic part of BP so it may better describe BP variability. However, only few studies addressed the role of PP variability (PPV) on stroke outcome and they only used single parameters 20 , 21 . Nevertheless they have found an association between high PPV and unfavorable clinical outcome in patients with AIS who underwent either IVT 20 as well as prognostic significance of PPV for AIS patients treated with MT 21 . The retrospective study in a group of patients not qualified to the thrombolytic treatment revealed also that PPV provided a prime predictor of bad outcome 22 . Hence, the aim of this study was to determine the relationship between PPV and stroke short- and long-term outcome using a variety of statistic parameters.

Study subjects and data collection

The study has a retrospective character and is based on an electronic database registered in our university stroke unit. The patients study group was collected in 2009–2011. Patients included in the study cohort were admitted to the hospital within 36 h from the onset of stroke symptoms. At admission, all patients were evaluated using National Institutes of Health Stroke Scale (NIHSS) and Glasgow Coma Scale 23 . At the same time, demographic data, comorbid conditions, history of previous cardiovascular diseases and baseline measures were collected. Further on, all patients underwent non-enhanced head computer tomography (CT) or magnetic resonance imaging (MRI) at admission and within 2–5 days after stroke onset. Patients were treated employing medication and care according to current national stroke guidelines 24 . From 227 patients collected in the database, 24 patients were excluded from further analysis because of insufficient data (lack of adequate number of BP measures in 14 patients, absence of follow-up visit in 6 patients) or death during the period of first 72 h post-stroke (4 patients). The flowchart in Fig.  1 shows the exclusions, which were mostly due to interruptions in successive four-hour-interval BP readings needed for the variability estimates. Patients’ written consent on participation in the study and collection of personal data was obtained during the time of hospitalization. Ethical approval for this study was issued by the Chairman of the Committee  on Bioethics, Poznan University of Medical Sciences (decision from 15.04.2021). This study was completed in accordance with the Helsinki Declaration as revised in 2013.

figure 1

Flowchart showing categories of patients excluded from the study.

BPV parameters

Blood pressure values were taken in the supine position in the non-paretic arm by a trained nurse using the Ultraview SL2600 monitoring system (Spacelabs Medical Inc., USA), which meets and exceeds the American National Standards Institute/Association for the Advancement of Medical Instrumentation (ANSI/AAMI) standard SP-10. In further analysis we used blood pressure measurements recorded in 4-h intervals from midnight after admission through next 72 h of hospitalization. PP was calculated as a difference between systolic and diastolic blood pressure (SBP—DBP). PPV was investigated using varied parameters, all employed in former studies 22 , 25 . The following formulae, where x i and \(\overline{x }\) stand for a single PP reading and the mean value of all readings, respectively, were exploited: standard deviation (SD, \(SD= \sqrt{1/(n-1){\sum_{i=1}^{n}({x}_{i}-\overline{x })}^{2}}\) ), coefficient of variation (CV, CV = 100xSD/ \(\overline{x }\) ), successive variation index (SV, \(SV=\sqrt{1/(n-1)\sum_{i=1}^{n-1}{({x}_{i+1}-{x}_{i})}^{2}}\) ), average real variability (ARV, \(ARV=1/(n-1){\sum }_{i=1}^{n-1}\left|{x}_{i+1}-{x}_{i}\right|\) ), difference maximum-minimum (DMM; difference between maximum and minimum PP value recorded) and maximal successive change (MSC; the highest difference between successive readings).

Functional outcome

The clinical outcome was assessed by trained neurologists at 30-days (short-term) and 90-days (long-term) follow-up, using the modified Rankin Scale (mRS). The functional assessment was preceded by an intraobserver reliability check comprising independent neurologists involved in prior studies 26 . Data on the degree of disability and independence in daily activities was gathered during a visit or by a telephone survey. It was obtained directly from the patients whenever possible, and otherwise from their caregivers. The functional outcome was dichotomized based on mRS score, unfavorable outcome was defined as mRS score ≥ 3 (dependance/death) while favorable outcome as mRS ≤ 2. This dichotomized division was used for the analyses employing the Mann–Whitney U test and in Model 1, 3 and 4 of logistic regression. In Model 2 of logistic regression, a ‘severity-adjusted’ outcome relying on admission NIHSS score, was employed 27 . In this case, the outcome was considered unfavorable when mRS score was 2–6 and NIHSS was ≤ 7, or mRS was 3–6 and NIHSS was between 8 and 14, or mRS was 4–6 and NIHSS was > 14. In other cases the outcome was considered favorable.

Statistical analysis

All data analysis was performed using Statistica 13.3 package (Tibco). All the items available in the database and relevant to the study were taken into account without prior sample size calculations. However, in our analysis the sample size is higher by more than one order of magnitude with respect to number of variables. Quantitative data were expressed as mean ± SD or median (interquartile range). Categorical variables were described as numbers (percentage). P values < 0.05 were considered statistically significant. To determine the relationship between PPV parameters and functional outcome in the groups considered, i.e. with/without thrombolytic treatment and the entire cohort, Spearman rank correlation test was used as a first approach. To assess the relationship, statistical significance of the correlation coefficients was invoked, considering moderate values of ρ S  < 0.4. Similar moderate correlations of BPV indicators were reported before in stroke studies 28 . The statistical significance of differences between the groups of patients with/without thrombolytic treatment were evaluated with the Mann–Whitney U test. The association between PPV parameters and unfavorable outcome was examined with multivariable logistic regression analysis. The Odds ratios (OR) and 95% confidence intervals (CIs) were calculated per 10 mmHg increase in PPV parameter, as recommended in prior systematic review 15 . The predictive significance of PPV parameters was determined using receiver operating characteristics (ROC) curve analysis. The area under the curve (AUC) values, Youden’s Index and cut-off points were reported.

Informed consent

Written informed consent was obtained from all subjects before the study (at the time of original data collection).

203 patients were included in the study, out of which 48 (20%) were subject to thrombolytic treatment. None of the patients included underwent MT therapy. From the whole cohort, 82 patients achieved unfavorable outcome (mRS ≥ 3) at 30-days and 79 at 90-days after stroke. Patients were more likely to be dependent or dead at 30 days if they were female, were older or had higher admission NIHSS (left side of Table 1 ).

This also applied to the group with unfavorable outcome at 90 days (right side of Table 1 ). Additionally, there was no association between admission BP values and stroke short- and long term outcome (Table 1 ).

The relationship between PPV and 30 and 90 days outcome was analyzed in the thrombolysis and non-thrombolysis subgroups as well as in the whole cohort, using the Spearman rank correlation test (Table 2 ). All PPV indices were significantly associated with mRS score after 30- and 90-days (p < 0.05), with the exception of CV in the thrombolysis group. Interestingly, higher Spearman ρ values were observed in patients after recanalization therapy (Spearman ρ = 0.455 for MSC after 90 days outcome). According to the Mann–Whitney U test, there was no difference between mean PPV values in the groups with- or without thrombolytic treatment (p > 0.05, right side of Table 2 ). Based on this result, further analyses were performed in the whole cohort (n = 203). We observed that correlations between mean PP values and mRS scores were only relevant in the group with thrombolytic treatment (Table 2 ).

The next step of the study was the logistic regression analysis for short- and long-term outcome. The unadjusted model demonstrated that all PPV indices were associated with risk of unfavorable outcome at 30 and 90 days after AIS (Table 3 , Model 1). In Model 2, all PPV indices were adjusted for their mean values and the application of recombined tissue plasminogen activator (rtPA). The PPV indices were independently associated with poor outcome at 30 days (OR = 4.457, 95% CI 1.961–10.132 per 10 mmHg increase in SD, p = 0.000; OR = 1.473, 95% CI 1.190–1.823 for 10 mmHg increase in MSC, p = 0.000) and 90 days (OR = 3.666, 95% CI 1.648–8.156 per 10 mmHg increase in SD, p = 0.001; OR 1.439, 95% CI 1.167–1.774 per 10 mmHg increase in MSC, p = 0.001). Very similar odds ratios (OR) were found for PPV indices when the ‘adjusted outcome’ was employed, which took into consideration both mRS and baseline NIHSS scores, as mentioned in the methods (Model 3). Model 4 was an extension of Model 2 adjusting for age, gender and history of myocardial infarction. We conclude that ORs for all PPV indicators remain statistically significant, although their values steadily decrease, passing from Model 1 to Model 4. The exception are PP ARV and PP SD indices, for which the ORs are not statistically significant in Model 4 as far as the long-term outcome is concerned (right-bottom corner of Table 3 ).

The quality and usefulness of PPV indices as predictors of unfavorable outcome was also examined by the ROC curve analysis (Table 4 ).

On the basis of AUC values, all PPV parameters were found relevant outcome predictors (p < 0.01). The highest values of AUC amounts to 0.669 for PP DMM index both at 30- and 90-days post-stroke (Fig.  2 ). We note, however, that only in this case the cut-off points are different for these two periods.

figure 2

ROC analysis of PP DMM and 30- and 90-days unfavorable outcome. AUC area under the curve, CI confidence interval, DMM difference maximum-minimum, PP pulse pressure, ROC receiver operating characteristic.

Our study demonstrates that increased PPV in the acute ischemic stroke phase was associated with both short- and long-term unfavorable outcome. Every 10 mmHg increase in the 72-h PPV indices leads to a higher likelihood of unfavorable outcome at 30- and 90-days post-stroke. The association of PPV with functional outcome held up even after adjustment for mean PP values, thrombolysis treatment and other baseline characteristics. We found that all PPV indices were valuable outcome predictors. Our study shows that DMM and MSC indices, which are very easy to compute, are as reliable as other, more complex indicators (CV, SV, SD, ARV). Therefore, DMM and MSC seem the most convenient in clinical practice.

Previous studies evaluated BPV most frequently with the use of SBP and MAP components and demonstrated the association of BPV with poor outcome 90 days after AIS 11 , 12 , 29 , 30 , 31 , 32 . This association was more evident in studies involving patients after reperfusion therapies 12 , 31 , 32 . Some studies found that BPV is associated with the post-stroke outcome only in those patients, who underwent thrombolysis treatment and who did not achieve vessel recanalization, based on imaging examination 30 , 33 . This led to the view that in patients with successful recanalization increased BPV is less of a concern. However, as opposed to MT, in IVT therapy the exact time of recanalization is unknown. Observational studies examining the impact of BPV in patients treated with MT demonstrated a general significant relationship with poor outcome or death, which was not limited to non-recanalized patients 12 , 31 .

The role of PPV in clinical practice is currently underestimated. Up to date only few studies examined the relationship between PPV and stroke outcome 20 , 21 , 22 , 25 . Our results are consistent with other recent reports. Katsanos et al. conducted a study on a group of thrombolysis-eligible patients and found that increased PPV was independently associated with both short- (24-h) and long-term (90 days) outcome 20 . Every 5 mmHg-increase in the 24-h PP SD was independently associated with a 36% decrease in the likelihood of 90-day independent functional outcome. We found the association between PPV and poor outcome in the whole cohort of patients, i.e. with and without thrombolysis treatment. 10 mmHg increase in PP SD was significantly associated with unfavorable outcome at 90 days (OR adjusted  = 3.666, 95% CI 1.648–8.156, p = 0.001). Notably, higher Spearman correlation ρ’s were obtained here in the group with thrombolytic treatment (ρ = 0.455 for PP MSC after 90 days outcome). Because of insufficient amount of patients with thrombolysis, we could not include this group in the logistic regression analysis. Another study concerning PPV was carried out on patients with large vessel occlusion stroke treated with MT 25 . In that investigation, PPV 24 h after IAT was associated with poor 3-month outcome and PPV indices had an excellent ability to predict unfavorable outcome (AUC 0.924 for SD). For comparison, in our study, the corresponding results for the whole cohort were lower (AUC 0.664 for SD), but high enough to provide predictive significance (p = 0.000). In both studies, DMM emerged as an equally reliable outcome predictor as more complex indices (CV, SV, SD).

It is worth emphasizing that the significance of PP as a predictor of cardiovascular risk is well established 16 , 34 , 35 , 36 . The relationship between PP and cerebrovascular incidents was less investigated. Lee et al. found that PP in the acute period of stroke had a nonlinear, J-shaped relation with major vascular events, or stroke recurrence 19 . Notably, the predictive power of PP was stronger than that of other commonly used BP parameters (SBP, MAP). Another study demonstrated a non-linear reverse J-curve association between the admission PP level and 3-month post-stroke functional outcomes 37 . By contrast, we have not found any connection between admission PP values and post-stroke outcome after 30 and 90 days.

While MAP is defined as an average blood pressure in aorta and its major branches during the cardiac cycle and it is nearly constant along the arterial tree, PP is considered as a pulsatile component of BP. MAP and PP are dependent variables, though different PP values may occur for a given MAP 38 . It is observed that PP increases markedly with age 39 . Among the causes affecting PP raise, in young individuals stroke volume and ventricular ejection is dominant, whereas in elderly, PP is mainly affected by a reduction in visco-elastic properties of arterial wall and the timing of wave reflection 38 , 40 . Hence, PP is commonly taken as a marker of arterial stiffness. Arterial stiffness was reported to be associated with resistance in cerebral circulation in elderly 41 . It is also suspected to lead to the impairment of the collateral circulation and therefore, to decrease the benefit of recanalization therapies in acute ischemic stroke. Thus future trials investigating the association between PPV and collateral circulation in acute stroke patients are highly needed. Systolic BPV was found to be associated with 90-days post-stroke outcome in patients with poor collateral status, but the data concerning PPV are lacking 42 , 43 .

In our study group, risk factors of achieving worse clinical outcome or death were (1) female sex and (2) previous myocardial infarction. We used those factors as confounders in our logistic regression analysis. The association between BPV and cardiovascular events is well documented. Increased long-term BPV is significantly associated with coronary heart disease incidents and cardiovascular mortality, independent of mean BP 44 , 45 . Greater short-term BPV after acute coronary syndromes is a predictor of major adverse cardiac events 46 , 47 . The impact of sex on the magnitude of BPV and further, on ischemic stroke outcome has not been studied to the best of our knowledge. Women tend to have higher SBP at the time of presentation with AIS and are more likely to have premorbid hypertension 48 . The underlying mechanism for the observed sex differences is not clear, however it has been proposed that female steroid hormones and autonomic dysregulation after menopause are likely to play a role 49 , 50 . There is a great discrepancy in stroke outcome, with women having more severe strokes, less favorable prognoses and greater incidence of death 51 , 52 . Therefore we cannot exclude the possibility that our data may have been influenced by sex differences. In future trials it would be interesting to systematically study the sex disparities in BPV and their association with stroke outcome.

Our study had several limitations. It was a retrospective analysis of a prospective single-center stroke database, which might lead to selection bias and limits the generalizability of the results. The sample size is relatively small in relation to prevalence of the stroke incidents and we have used BP measurements in 4-h intervals, which is larger than suggested in recent literature 13 . The database does not contain the results of imaging techniques (CT, MRI) so that we could not consider secondary outcomes such as symptomatic intracerebral hemorrhage or cerebral infarct volume. In addition, it does not provide information about implemented drug treatments, which is a factor possibly affecting BP values. Importantly, we used standard therapy according to the guidelines, so we don’t expect significant differences in the influence of antihypertension therapy on outcomes in similar cases. As to the adjusted regression analysis, a limited number of clinical cofounders was taken into account, which is also a limitation of our study. The outcome of the study may be also affected by a bias arising from subjective assessments of the caregivers.

Notwithstanding the foregoing, our study confirms the significance of Pulse Pressure Variability in predicting the functional outcome in AIS, both short and long-term. It brings novel insights concerning the usefulness of six different PPV indices (CV, SV, SD, ARV, DMM and MSC), while most studies focus only on two. The provided analysis is comprehensive and combines a wider range of statistical methods than previous studies. The issues addressed here aids the recent search of the best BPV measures applicable in clinical practice and more importantly, provide the predictors of the functional outcome after ischemic stroke treatment.

In conclusion, elevated PPV during the first 72 h after admission as a result of AIS occurrence is associated with unfavorable outcome at 30 and 90 days, and this association is independent of mean BP levels. All considered PPV measures are reliable stroke outcome predictors. Our recommendation is that clinical trials investigating the benefit of reducing BPV by using antihypertensive medication should monitor also PPV values.

Data availability

Data and materials are available on request to the corresponding author.

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R.K. conceived the study. M.K.M. was involved in data analysis and wrote the first draft of the manuscript. R.K. reviewed and edited the manuscript. Both authors approved the final version of the manuscript.

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Kamieniarz-Mędrygał, M., Kaźmierski, R. Significance of pulse pressure variability in predicting functional outcome in acute ischemic stroke: a retrospective, single-center, observational cohort study. Sci Rep 13 , 3618 (2023). https://doi.org/10.1038/s41598-023-30648-2

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Observational Study: Definitions And Variations


There are many different types of studies out there, each one serving its own unique purpose. Sometimes, it’s difficult to research specific events or phenomena without manipulating an element of the study to produce the results.

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Observational Study Definition

According to a dictionary of medical-related terms by Cancer.gov , the definition of observational study is as follows:

“A type of study in which individuals are observed or certain outcomes are measured. No attempt is made to affect the outcome (for example, no treatment is given).”

In other words, unlike other studies where doctors or scientists attempt to conduct research on things like the effect of new drugs or treatments on a person or illness, in an observational study, no outside factors are added into the mix. In that way, observational studies are not like experiments where external elements usually play a big role.

In an observational study, researchers will attempt to look at a treatment, risk factor, test, or some other type of intervention without changing who is exposed to it. Researchers will make no attempts to manipulate the study in any way, and, as the name implies, their goal with this kind of study is just to observe.

Types of Observational Studies

There are a few different types of observational studies: cohort studies, case-control studies, and experimental studies. Let’s take a closer look at each one.

Cohort studies

In a cohort study, researchers look at people who are linked in a specific way. For example, a birth cohort is a group of people that are born around the same time. Researchers will observe a group to see what happens when each individual is exposed to a certain variable compared to others who aren’t exposed.

Case-control studies

In a case-control study, researchers look for participants with existing health problems, or cases, and then contrast them with other people who don’t have that same problem. They then compare the two groups in relation to different exposures.

Experimental studies

In experimental studies, researchers will introduce some form of intervention and then observe the effects. For example, in a randomized control trial, people are randomly divided into two groups where one group will receive an intervention, like a new drug, and the second will receive nothing or a placebo. Then, researchers will look at what happens to people in both groups and note any differences.

The Strengths and Weaknesses of Observational Studies

two men in a lab conducting an observational study

Like anything else, observational studies have both strengths and weaknesses. At times, these types of studies are the best way for researchers to look at a certain topic, especially when ethics are involved.

For example, you wouldn’t necessarily expose participants to something harmful or potentially fatal for the purpose of research.

One of the strengths of observational studies is that it is the most efficient way to research certain things, like rare conditions, when applying case-control studies. At other times, cohort studies can be really effective if you’re able to conduct the research over a longer period of time.

That being said, observational studies aren’t without their weaknesses, more specifically, they’re open to bias. As an example, in a cohort study, you might look at the link between elevated blood pressure and people who exercise frequently. However, the bias might be that the people who exercise frequently are already leading a healthier lifestyle and might have better diets than the second group.

Observational Studies and Ethics

As we briefly touched upon earlier, it wouldn’t be ethical to run any type of trial on unsuspecting participants. There are some things that would be considered medically unethical to do in an observational study.

As an example, it would be difficult to conduct a trial of the long-term effects of smoking on the lungs and overall health. In such a study, participants would have to be randomized and put into two groups: those who smoke and those who don’t. These groups would then be observed over time. However, since the long-term effects include things like lung cancer or death, the ethics here are questionable.

This experiment would work if researchers simply surveyed and observed the smoking status of people who are already smokers, but then the test wouldn’t be randomized and other factors and biases can be at play.

Examples of Observational Studies

In order to have a better understanding of observational studies, let’s take a look at some examples.

A simple example of an observational study is someone surveying people on a busy street about any topic, such as their favorite food. Then, this person would take that information and see if there are enough restaurants serving the most commonly answered foods in that area. In this situation, the researcher is simply observing the answers but not influencing the results.

In a second example of an observational study, a researcher might be studying the effects of eating sugary food on a person’s health. In a test group of 400 people, 200 of them will eat sugary foods over a year while another 200 won’t, and then each group will be observed. At the end of the test, each group will be assessed for overall health, and the data will be analyzed in order to come to some conclusion. Again, the researcher here hasn’t manipulated the study itself, just observed the participants.

Hopefully, after reading the observational study definition, examples, and other details, you have a better idea of how it’s used in science and research.

If you’re interested in conducting observational studies and research in your career, then you should start out with a degree in health science . At University of the People, we offer an entirely tuition-free and online health science degree so that you can study the basics before learning more about specific research methods.

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The 90s sit-com Seinfeld is often called "a show about nothing." Lauded for its observational humor, this quick-witted show focussed on four hapless New Yorkers navigating work, relationships...yada yada yada.

Jerry, George, Elaine & Kramer set themselves apart from the characters who populated shows like Friends or Cheers , by being the exact opposite of the characters audiences would normally root for. These four New Yorkers were overly analytical, calculating, and above all, selfish.

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The economics lessons in kids' books

In other words, they had all the makings of a fascinating case study in economics.

Economics professors Linda Ghent and Alan Grant went so far as to write an entire book on the subject, Seinfeld & Economics . The book points readers to economic principles that appear throughout the show, ideas like economic utility, game theory, and the best way to allocate resources in the face of scarcity.

On today's show, we make the case that Seinfeld is, at its heart, not a show about nothing, but a show about economics. And that understanding Seinfeld can change the way you understand economics itself.

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Press Release

Electrocardiograph (ecg) market expert business study report 2023-2028.

The MarketWatch News Department was not involved in the creation of this content.

Mar 06, 2023 (The Expresswire) -- [95 Pages] Top “ Electrocardiograph (ECG) Market ” Size 2023 Key players Profiled in the Report are [, GE Healthcare , Philips Healthcare , Nihon Kohden , Schiller , Opto Circuits , Johnson and Johnson , Mindray Medical , Medtronic ,] most important, influential, or successful companies, brands, or individuals within a Electrocardiograph (ECG) market 2023 to 2028.

Short Description About Electrocardiograph (ECG) Market:

Complete Overview of the Global Electrocardiograph (ECG) Market: - Providing a complete overview of the global Electrocardiograph (ECG) market is a complex task, as there are many different markets and industries around the world. However, I can provide a high-level summary of some of the key trends and factors that are currently impacting the global Electrocardiograph (ECG) market. Economic Growth, Technology, E-commerce, Globalization, Sustainability, Demographics, Political and regulatory risks These are just a few of the many factors that are currently shaping the global market. It is a dynamic and ever-changing environment, and businesses that are able to adapt to new trends and challenges are likely to be the most successful.

Electrocardiograph (ECG) Market Provides High-class Data: - It is true that the global Electrocardiograph (ECG) market provides a wealth of high-quality data for businesses and investors to analyse and make informed decisions. There are many different sources of market data, including government statistics, industry reports, financial news, and market research firms. Some of the key types of data that are available from the global Electrocardiograph (ECG) market include, Economic data, Financial data, Industry data, Consumer data However, it is important to carefully evaluate the quality and reliability of data sources and to use multiple sources of data to gain a more complete understanding of the Electrocardiograph (ECG) market.

Get a Sample Copy of the Electrocardiograph (ECG) Report 2023

Top Country Data and Analysis: - for United States, Canada, Mexico, Germany, France, United Kingdom, Russia, Italy, China, Japan, Korea, India, Southeast Asia, Australia, Brazil and Saudi Arabia, etc. It also throws light on the progress of key regional Electrocardiograph (ECG) Markets such as North America, Europe, Asia-Pacific, South America and Middle East and Africa

Description and Analysis of Electrocardiograph (ECG) market : - Electrocardiograph (ECG) Market analysis is the process of evaluating market conditions and trends in order to make informed business decisions. A market can refer to a specific geographic location, particular industry or sector, develop strategies for entering or expanding in a particular Electrocardiograph (ECG) market.

Electrocardiograph (ECG) Market analysis can also involve forecasting future market trends and conditions, based on factors like technological change, regulatory developments, or demographic shifts. This can be used to develop long-term strategic plans and to identify potential risks and opportunities for growth.

Overall, market analysis is an important tool for businesses looking to enter or expand in a particular Electrocardiograph (ECG) market. By carefully evaluating Electrocardiograph (ECG) market conditions and trends, businesses can make more informed decisions and develop strategies that are better aligned with customer needs and Electrocardiograph (ECG) market opportunities.

Get a Sample Copy of the Report at - https://www.industryresearch.biz/enquiry/request-sample/20309212

Market Analysis and Insights: Global Electrocardiograph (ECG) Market Due to the COVID-19 pandemic, the global Electrocardiograph (ECG) market size is estimated to be worth USD million in 2022 and is forecast to a readjusted size of USD million by 2028 with a CAGR of Percent during the review period. Fully considering the economic change by this health crisis, Monitoring ECG Systems accounting for Percent of the Electrocardiograph (ECG) global market in 2021, is projected to value USD million by 2028, growing at a revised Percent CAGR in the post-COVID-19 period. While Home-Based Users segment is altered to an Percent CAGR throughout this forecast period. China Electrocardiograph (ECG) market size is valued at USD million in 2021, while the US and Europe Electrocardiograph (ECG) are USD million and USD million, severally. The proportion of the US is Percent in 2021, while China and Europe are Percent and Percent respectively, and it is predicted that China proportion will reach Percent in 2028, trailing a CAGR of Percent through the analysis period. Japan, South Korea, and Southeast Asia are noteworthy markets in Asia, with CAGR Percent, Percent, and Percent respectively for the next 6-year period. As for the Europe Electrocardiograph (ECG) landscape, Germany is projected to reach USD million by 2028 trailing a CAGR of Percent over the forecast period. The global key manufacturers of Electrocardiograph (ECG) include GE Healthcare, Philips Healthcare, Nihon Kohden, Schiller, Opto Circuits, Johnson and Johnson, Mindray Medical and Medtronic, etc. In 2021, the global top five players have a share approximately Percent in terms of revenue. Global Electrocardiograph (ECG) Scope and Segment Electrocardiograph (ECG) market is segmented by Type and by Application. Players, stakeholders, and other participants in the global Electrocardiograph (ECG) market will be able to gain the upper hand as they use the report as a powerful resource. The segmental analysis focuses on sales, revenue and forecast by Type and by Application for the period 2017-2028.

Inquire or Share Your Questions If Any Before the Purchasing This Report https://www.industryresearch.biz/enquiry/pre-order-enquiry/20309212

Market segment by Region/Country including: -

User center of Electrocardiograph (ECG) market 2023

Yes. As the COVID-19 and the Russia-Ukraine war are profoundly affecting the global supply chain relationship and raw material price system, we have definitely taken them into consideration throughout the research, and we elaborate at full length on the impact of the pandemic and the war on the Precious Metals Industry.

Final Report will add the analysis of the impact of COVID-19 on this industry.


The Global Electrocardiograph (ECG) market is anticipated to rise at a considerable rate during the forecast period. the market is growing at a steady rate and with the rising adoption of strategies by key players, the market is expected to rise over the projected horizon.

Electrocardiograph (ECG) Market -SegmentationAnalysis:

Report further studies the market development status and future Electrocardiograph (ECG) Market trend across the world. Also, it splits Electrocardiograph (ECG) market Segmentation by Type and by Applications to fully and deeply research and reveal market profile and prospects.

Segment by Type

Which growth factors drives the Electrocardiograph (ECG) market growth?

Increasing use of is expected to drive the growth of the Electrocardiograph (ECG) Market.

Segment by Application

Which market dynamics affect the business?

The report provides a detailed evaluation of the market by highlighting information on different aspects which include drivers, restraints, opportunities, and threats. This information can help stakeholders to make appropriate decisions before investing.

It also provides accurate information and cutting-edge analysis that is necessary to formulate an ideal business plan, and to define the right path for rapid growth for all involved industry players. With this information, stakeholders will be more capable of developing new strategies, which focus on market opportunities that will benefit them, making their business endeavors profitable in the process.

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Electrocardiograph (ECG) Market - Competitive Analysis:

With the aim of clearly revealing the competitive situation of the industry, we concretely analyze not only the leading enterprises that have a voice on a global scale, but also the regional small and medium-sized companies that play key roles and have plenty of potential growth. Please find the key player list in Summary.

Electrocardiograph (ECG) Industry leading players are the ones that have the biggest impact, the most market shares 2023, the best reputation, or the highest revenue within their field they are

Who are the Leading Players in Electrocardiograph (ECG) Market?

Both Primary and Secondary data sources are being used while compiling the report. Primary sources include extensive interviews of key opinion leaders and industry experts (such as experienced front-line staff, directors, CEOs, and marketing executives), downstream distributors, as well as end-users. Secondary sources include the research of the annual and financial reports of the top companies, public files, new journals, etc. We also cooperate with some third-party databases.

Please find a more complete list of data sources in Chapters:

1.To study and analyze the global Electrocardiograph (ECG) consumption (value) by key regions/countries, product type and application

2.To understand the structure of Electrocardiograph (ECG) Market by identifying its various sub segments.

3.Focuses on the key global Electrocardiograph (ECG) manufacturers, to define, describe and analyze the value, market share, market competition landscape, Porter's five forces analysis, SWOT analysis and development plans in next few years.

4.To analyze the Electrocardiograph (ECG) with respect to individual growth trends, future prospects, and their contribution to the total market.

5.To share detailed information about the key factors influencing the growth of the market (growth potential, opportunities, drivers, industry-specific challenges and risks).

6.To project the consumption of Electrocardiograph (ECG) submarkets, with respect to key regions (along with their respective key countries).

7.To analyze competitive developments such as expansions, agreements, new product launches, and acquisitions in the market.

8.To strategically profile the key players and comprehensively analyze their growth strategies.

Major Points from Table of Contents

1 Market Overview

1.1 Electrocardiograph (ECG) Introduction

1.2 Market Analysis by Type

1.3 Market Analysis by Applications

1.4 Market Analysis by Regions

1.5 Market Dynamics

2 Manufacturers Profiles

3 Global Electrocardiograph (ECG) Market Competition, by Manufacturer

4 Global Electrocardiograph (ECG) Market Analysis by Regions

5 North America Electrocardiograph (ECG) by Countries

6 Europe Electrocardiograph (ECG) by Countries

7 Asia-Pacific Electrocardiograph (ECG) by Countries

8 Latin America, Middle and Africa Electrocardiograph (ECG) by Countries

9 Electrocardiograph (ECG) Market Segment by Type

10 Electrocardiograph (ECG) Market Segment by Application

11 Electrocardiograph (ECG) Market Forecast (2023-2028)

12 Sales Channel, Distributors, Traders and Dealers

13 Appendix

13.1 Methodology

13.2 Data Source

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  1. How to Do Observation Research

    observational study and case reports

  2. Study Report Writing

    observational study and case reports

  3. Types of Epidemiological Studies used in the field.

    observational study and case reports

  4. Observational Study in Statistics

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  5. 17+ Case Study Templates

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  6. Observational Studies

    observational study and case reports


  1. Observations, Ideas and Evidence 2

  2. Observational Studies (Part 1)

  3. Approaching Case Studies

  4. What are observational studies? #shorts #research #science

  5. Observational Research Introduction

  6. How italian online media report on observational research. A case study


  1. What Is an Observational Study?

    An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups. These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes.

  2. Principles of Epidemiology

    In an observational cohort study, subjects are enrolled or grouped on the basis of their exposure, then are followed to document occurrence of disease. Differences in disease rates between the exposed and unexposed groups lead investigators to conclude that exposure is associated with disease.

  3. Case Study Observational Research: A Framework for Conducting Case

    Case study research is a comprehensive method that incorporates multiple sources of data to provide detailed accounts of complex research phenomena in real-life contexts. However, current models of case study research do not particularly distinguish the unique contribution observation data can make.

  4. Case Report

    Case reports are considered the lowest level of evidence, but they are also the first line of evidence, because they are where new issues and ideas emerge. This is why they form the base of our pyramid. A good case report will be clear about the importance of the observation being reported.

  5. Case Reports and Case Series

    A case report is a detailed description of disease occurrence in a single person. Unusual features of the case may suggest a new hypothesis about the causes or mechanisms of disease. Example: Acquired Immunodeficiency in an Infant; Possible Transmission by Means of Blood Products

  6. PDF Case Study Observational Research: A Framework for Conducting Case

    potential to reach beyond other methods that rely largely or solely on self-report. This article describes the distinctive characteristics of case study observational research, a modified form of Yin's 2014 model of case study research the authors used in a study exploring interprofessional collaboration in primary care.


    We considered it reasonable to initially restrict the recommendations to the three main analytical designs that are used in observational research: cohort, case-control, and cross-sectional studies. We want to provide guidance on how to report observational research well. Our recommendations are not prescriptions for designing or conducting ...

  8. Observational Studies I Case Report, Case Series, Case ...

    Case-control Study observational epidemiologic study with a comparison group -subjects are grouped according to disease status -diseased -not diseases collect exposure information (historical) on subjects classify subjects according to exposure status -exposed -not exposed organize info into 2 by 2 table Selecting Cases

  9. 3.2

    Case reports and case-series are uncontrolled observational studies. Case Report. A case report only demonstrates that a clinical event of interest is possible. In a case report, there is no control of treatment assignment, endpoint ascertainment, or confounders. There is no control group for the sake of comparison.

  10. Observational Studies: Cohort and Case-Control Studies

    ese types of questions. Well-designed observational studies have been shown to provide results similar to those of randomized controlled trials, challenging the belief that observational studies are second rate. Cohort studies and case-control studies are two primary types of observational studies that aid in evaluating associations between diseases and exposures. In this review article, the ...

  11. 6 Essential Tips for Writing a Field Study Report

    A field study report focuses on factual and observational details of a project case. It must help the reader understand how theory applies to real-world scenarios. Hence, it should cover the circumstances and contributing factors to derive conclusive results from the observed and collated raw data.

  12. Frontiers

    Case reports/studies and case series do not include controls and so, to be considered in a systematic review, these types of studies will need to be well-documented with respect to treatment or other contextual factors that may explain or influence the outcome.

  13. Case Report vs Cross-Sectional Study: A Simple Explanation

    Yes, sometimes the case report involves following the patient over a period of time. No, in a cross-sectional study the exposure and the outcome are measured simultaneously and so there is no need to follow participants over time. Example. In 1991, Fred Kern, Jr. reported the case of an 88-year-old man who has been eating 20-30 eggs each day ...

  14. Observational descriptive study: case report, case series ...

    Observational descriptive study: case report, case series & ecological study Aug. 01, 2021 • 7 likes • 2,764 views Data & Analytics This presentation provides overview on observational study designs Prabesh Ghimire Follow Public Health Professional (MPH) Advertisement Advertisement Recommended 2. Case study and case series Razif Shahril 39.3k views

  15. 6.5 Observational Research

    Like many observational research methods, case studies tend to be more qualitative in nature. Case study methods involve an in-depth, and often a longitudinal examination of an individual. Depending on the focus of the case study, individuals may or may not be observed in their natural setting.

  16. Home: Finding Types of Research: Observational Studies

    From the Publication Type scroll menu, select either Case Reports, Case Study, or Observational Study . After you have your results, make sure to read through the article abstracts to see which type of study design the researchers are using. There is no guarantee that the database will only generate these types of studies.

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  19. Safety and tolerability of nabiximols oromucosal spray: a review of

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  20. Significance of pulse pressure variability in predicting functional

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  21. Structural brain changes in patients with post-COVID fatigue: a

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  22. Observational Study: Definitions and Variations

    In an observational study, researchers will attempt to look at a treatment, risk factor, test, or some other type of intervention without changing who is exposed to it. Researchers will make no attempts to manipulate the study in any way, and, as the name implies, their goal with this kind of study is just to observe. Types of Observational Studies

  23. Nowait

    Published Mar 5, 2023. + Follow. The Norwait study is an example of an observational study where procedural failings have resulted in patients with active cancers being included in an ...

  24. Exploring Seinfeld through the lens of economics : Planet Money

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  25. Electrocardiograph (ECG) Market Expert Business Study Report 2023-2028

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