case control studies pros and cons

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Case-control and Cohort studies: A brief overview

Posted on 6th December 2017 by Saul Crandon

Introduction

Case-control and cohort studies are observational studies that lie near the middle of the hierarchy of evidence . These types of studies, along with randomised controlled trials, constitute analytical studies, whereas case reports and case series define descriptive studies (1). Although these studies are not ranked as highly as randomised controlled trials, they can provide strong evidence if designed appropriately.

Case-control studies

Case-control studies are retrospective. They clearly define two groups at the start: one with the outcome/disease and one without the outcome/disease. They look back to assess whether there is a statistically significant difference in the rates of exposure to a defined risk factor between the groups. See Figure 1 for a pictorial representation of a case-control study design. This can suggest associations between the risk factor and development of the disease in question, although no definitive causality can be drawn. The main outcome measure in case-control studies is odds ratio (OR) .

Figure 1. Case-control study design.

Cases should be selected based on objective inclusion and exclusion criteria from a reliable source such as a disease registry. An inherent issue with selecting cases is that a certain proportion of those with the disease would not have a formal diagnosis, may not present for medical care, may be misdiagnosed or may have died before getting a diagnosis. Regardless of how the cases are selected, they should be representative of the broader disease population that you are investigating to ensure generalisability.

Case-control studies should include two groups that are identical EXCEPT for their outcome / disease status.

As such, controls should also be selected carefully. It is possible to match controls to the cases selected on the basis of various factors (e.g. age, sex) to ensure these do not confound the study results. It may even increase statistical power and study precision by choosing up to three or four controls per case (2).

Case-controls can provide fast results and they are cheaper to perform than most other studies. The fact that the analysis is retrospective, allows rare diseases or diseases with long latency periods to be investigated. Furthermore, you can assess multiple exposures to get a better understanding of possible risk factors for the defined outcome / disease.

Nevertheless, as case-controls are retrospective, they are more prone to bias. One of the main examples is recall bias. Often case-control studies require the participants to self-report their exposure to a certain factor. Recall bias is the systematic difference in how the two groups may recall past events e.g. in a study investigating stillbirth, a mother who experienced this may recall the possible contributing factors a lot more vividly than a mother who had a healthy birth.

A summary of the pros and cons of case-control studies are provided in Table 1.

Table 1. Advantages and disadvantages of case-control studies.

Cohort studies

Cohort studies can be retrospective or prospective. Retrospective cohort studies are NOT the same as case-control studies.

In retrospective cohort studies, the exposure and outcomes have already happened. They are usually conducted on data that already exists (from prospective studies) and the exposures are defined before looking at the existing outcome data to see whether exposure to a risk factor is associated with a statistically significant difference in the outcome development rate.

Prospective cohort studies are more common. People are recruited into cohort studies regardless of their exposure or outcome status. This is one of their important strengths. People are often recruited because of their geographical area or occupation, for example, and researchers can then measure and analyse a range of exposures and outcomes.

The study then follows these participants for a defined period to assess the proportion that develop the outcome/disease of interest. See Figure 2 for a pictorial representation of a cohort study design. Therefore, cohort studies are good for assessing prognosis, risk factors and harm. The outcome measure in cohort studies is usually a risk ratio / relative risk (RR).

Figure 2. Cohort study design.

Cohort studies should include two groups that are identical EXCEPT for their exposure status.

As a result, both exposed and unexposed groups should be recruited from the same source population. Another important consideration is attrition. If a significant number of participants are not followed up (lost, death, dropped out) then this may impact the validity of the study. Not only does it decrease the study’s power, but there may be attrition bias – a significant difference between the groups of those that did not complete the study.

Cohort studies can assess a range of outcomes allowing an exposure to be rigorously assessed for its impact in developing disease. Additionally, they are good for rare exposures, e.g. contact with a chemical radiation blast.

Whilst cohort studies are useful, they can be expensive and time-consuming, especially if a long follow-up period is chosen or the disease itself is rare or has a long latency.

A summary of the pros and cons of cohort studies are provided in Table 2.

The Strengthening of Reporting of Observational Studies in Epidemiology Statement (STROBE)

STROBE provides a checklist of important steps for conducting these types of studies, as well as acting as best-practice reporting guidelines (3). Both case-control and cohort studies are observational, with varying advantages and disadvantages. However, the most important factor to the quality of evidence these studies provide, is their methodological quality.

Song, J. and Chung, K. Observational Studies: Cohort and Case-Control Studies .Â Plastic and Reconstructive Surgery. Â 2010 Dec;126(6):2234-2242.
Ury HK. Efficiency of case-control studies with multiple controls per case: Continuous or dichotomous data .Â Biometrics .Â 1975 Sep;31(3):643â€“649.
von Elm E, Altman DG, Egger M, Pocock SJ, GÃ¸tzsche PC, Vandenbroucke JP; STROBE Initiative.Â The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement: guidelines for reporting observational studies. Â Lancet 2007 Oct;370(9596):1453-14577. PMID: 18064739.

Saul Crandon

No Comments on Case-control and Cohort studies: A brief overview

Very well presented, excellent clarifications. Has put me right back into class, literally!

Very clear and informative! Thank you.

very informative article.

Thank you for the easy to understand blog in cohort studies. I want to follow a group of people with and without a disease to see what health outcomes occurs to them in future such as hospitalisations, diagnoses, procedures etc, as I have many health outcomes to consider, my questions is how to make sure these outcomes has not occurred before the “exposure disease”. As, in cohort studies we are looking at incidence (new) cases, so if an outcome have occurred before the exposure, I can leave them out of the analysis. But because I am not looking at a single outcome which can be checked easily and if happened before exposure can be left out. I have EHR data, so all the exposure and outcome have occurred. my aim is to check the rates of different health outcomes between the exposed)dementia) and unexposed(non-dementia) individuals.

Very helpful information

Thanks for making this subject student friendly and easier to understand. A great help.

Thanks a lot. It really helped me to understand the topic. I am taking epidemiology class this winter, and your paper really saved me.

Happy new year.

Wow its amazing n simple way of briefing ,which i was enjoyed to learn this.its very easy n quick to pick ideas .. Thanks n stay connected

Saul you absolute melt! Really good work man

am a student of public health. This information is simple and well presented to the point. Thank you so much.

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Quite informative thank you so much for the info please continue posting. An mph student with Africa university Zimbabwe.

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Great to hear, thank you AJ!

I have recently completed an investigational study where evidence of phlebitis was determined in a control cohort by data mining from electronic medical records. We then introduced an intervention in an attempt to reduce incidence of phlebitis in a second cohort. Again, results were determined by data mining. This was an expedited study, so there subjects were enrolled in a specific cohort based on date(s) of the drug infused. How do I define this study? Thanks so much.

thanks for the information and knowledge about observational studies. am a masters student in public health/epidemilogy of the faculty of medicines and pharmaceutical sciences , University of Dschang. this information is very explicit and straight to the point

Very much helpful

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What Is a Case-Control Study? | Definition & Examples

What Is a Case-Control Study? | Definition & Examples

Published on February 4, 2023 by Tegan George . Revised on June 22, 2023.

A case-control study is an experimental design that compares a group of participants possessing a condition of interest to a very similar group lacking that condition. Here, the participants possessing the attribute of study, such as a disease, are called the “case,” and those without it are the “control.”

It’s important to remember that the case group is chosen because they already possess the attribute of interest. The point of the control group is to facilitate investigation, e.g., studying whether the case group systematically exhibits that attribute more than the control group does.

When to use a case-control study, examples of case-control studies, advantages and disadvantages of case-control studies, other interesting articles, frequently asked questions.

Case-control studies are a type of observational study often used in fields like medical research, environmental health, or epidemiology. While most observational studies are qualitative in nature, case-control studies can also be quantitative , and they often are in healthcare settings. Case-control studies can be used for both exploratory and explanatory research , and they are a good choice for studying research topics like disease exposure and health outcomes.

A case-control study may be a good fit for your research if it meets the following criteria.

Data on exposure (e.g., to a chemical or a pesticide) are difficult to obtain or expensive.
The disease associated with the exposure you’re studying has a long incubation period or is rare or under-studied (e.g., AIDS in the early 1980s).
The population you are studying is difficult to contact for follow-up questions (e.g., asylum seekers).

Retrospective cohort studies use existing secondary research data, such as medical records or databases, to identify a group of people with a common exposure or risk factor and to observe their outcomes over time. Case-control studies conduct primary research , comparing a group of participants possessing a condition of interest to a very similar group lacking that condition in real time.

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Case-control studies are common in fields like epidemiology, healthcare, and psychology.

You would then collect data on your participants’ exposure to contaminated drinking water, focusing on variables such as the source of said water and the duration of exposure, for both groups. You could then compare the two to determine if there is a relationship between drinking water contamination and the risk of developing a gastrointestinal illness. Example: Healthcare case-control study You are interested in the relationship between the dietary intake of a particular vitamin (e.g., vitamin D) and the risk of developing osteoporosis later in life. Here, the case group would be individuals who have been diagnosed with osteoporosis, while the control group would be individuals without osteoporosis.

You would then collect information on dietary intake of vitamin D for both the cases and controls and compare the two groups to determine if there is a relationship between vitamin D intake and the risk of developing osteoporosis. Example: Psychology case-control study You are studying the relationship between early-childhood stress and the likelihood of later developing post-traumatic stress disorder (PTSD). Here, the case group would be individuals who have been diagnosed with PTSD, while the control group would be individuals without PTSD.

Case-control studies are a solid research method choice, but they come with distinct advantages and disadvantages.

Advantages of case-control studies

Case-control studies are a great choice if you have any ethical considerations about your participants that could preclude you from using a traditional experimental design .
Case-control studies are time efficient and fairly inexpensive to conduct because they require fewer subjects than other research methods .
If there were multiple exposures leading to a single outcome, case-control studies can incorporate that. As such, they truly shine when used to study rare outcomes or outbreaks of a particular disease .

Disadvantages of case-control studies

Case-control studies, similarly to observational studies, run a high risk of research biases . They are particularly susceptible to observer bias , recall bias , and interviewer bias.
In the case of very rare exposures of the outcome studied, attempting to conduct a case-control study can be very time consuming and inefficient .
Case-control studies in general have low internal validity and are not always credible.

Case-control studies by design focus on one singular outcome. This makes them very rigid and not generalizable , as no extrapolation can be made about other outcomes like risk recurrence or future exposure threat. This leads to less satisfying results than other methodological choices.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

Student’s t -distribution
Normal distribution
Null and Alternative Hypotheses
Chi square tests
Confidence interval
Quartiles & Quantiles
Cluster sampling
Stratified sampling
Data cleansing
Reproducibility vs Replicability
Peer review
Prospective cohort study

Research bias

Implicit bias
Cognitive bias
Placebo effect
Hawthorne effect
Hindsight bias
Affect heuristic
Social desirability bias

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A case-control study differs from a cohort study because cohort studies are more longitudinal in nature and do not necessarily require a control group .

While one may be added if the investigator so chooses, members of the cohort are primarily selected because of a shared characteristic among them. In particular, retrospective cohort studies are designed to follow a group of people with a common exposure or risk factor over time and observe their outcomes.

Case-control studies, in contrast, require both a case group and a control group, as suggested by their name, and usually are used to identify risk factors for a disease by comparing cases and controls.

A case-control study differs from a cross-sectional study because case-control studies are naturally retrospective in nature, looking backward in time to identify exposures that may have occurred before the development of the disease.

On the other hand, cross-sectional studies collect data on a population at a single point in time. The goal here is to describe the characteristics of the population, such as their age, gender identity, or health status, and understand the distribution and relationships of these characteristics.

Cases and controls are selected for a case-control study based on their inherent characteristics. Participants already possessing the condition of interest form the “case,” while those without form the “control.”

Keep in mind that by definition the case group is chosen because they already possess the attribute of interest. The point of the control group is to facilitate investigation, e.g., studying whether the case group systematically exhibits that attribute more than the control group does.

The strength of the association between an exposure and a disease in a case-control study can be measured using a few different statistical measures , such as odds ratios (ORs) and relative risk (RR).

No, case-control studies cannot establish causality as a standalone measure.

As observational studies , they can suggest associations between an exposure and a disease, but they cannot prove without a doubt that the exposure causes the disease. In particular, issues arising from timing, research biases like recall bias , and the selection of variables lead to low internal validity and the inability to determine causality.

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2023, June 22). What Is a Case-Control Study? | Definition & Examples. Scribbr. Retrieved November 20, 2023, from https://www.scribbr.com/methodology/case-control-study/

Schlesselman, J. J. (1982). Case-Control Studies: Design, Conduct, Analysis (Monographs in Epidemiology and Biostatistics, 2) (Illustrated). Oxford University Press.

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Cohort and Case-Control Studies: Pro’s and Con’s

by guest contributer 16 Comments

Two designs commonly used in epidemiology are the cohort and case-control studies. Both study causal relationships between a risk factor and a disease. What is the difference between these two designs? And when should you opt for the one or the other?

Cohort studies

Cohort studies begin with a group of people (a cohort) free of disease. The people in the cohort are grouped by whether or not they are exposed to a potential cause of disease. The whole cohort is followed over time to see if the development of new cases of the disease (or other outcome) differs between the groups with and without exposure.

For example, you could do a cohort study if you suspect there might be a causal relationship between the use of a certain water source and the incidence of diarrhea among children under five in a village with different water sources.

You select a group of children under five years, either all children of that age in the village, a random sample taken from the population register, or e.g. children living in the same area, or attending the same clinic. Then you classify them as either using the suspected water source or other water sources. You check e.g. after two weeks whether the children have had diarrhea.

You can then calculate how many diarrhea cases there were among those children using the suspected water source and those using other sources of water supply (cumulative incidence of diarrhea).

Case-control studies

The same problem could also be studied in a case-control study. A case-control study begins with the selection of cases (people with a disease) and controls (people without the disease). The controls should represent people who would have been study cases if they had developed the disease (population at risk).

The exposure status to a potential cause of disease is determined for both cases and controls. Then the occurrence of the possible cause of the disease could be calculated for both the cases and controls. To come back to the example, you may compare children who present themselves at a health center with diarrhea (cases) with children with other complaints, for example acute respiratory infections (controls). You determine which source of drinking water they had used. Then calculate the proportion of cases and controls that were exposed to the suspected water source.

Pro’s and con’s

On what basis do you decide to choose a cohort design or a case-control design ?

Cohort studies provide the best information about the causation of disease because you follow persons from exposure to the occurrence of the disease. With data from cohort studies you can calculate cumulative incidences. Cumulative incidences are the most direct measurement of the risk of developing disease.

An added advantage is that you can examine a range of outcomes/diseases caused by one exposure. For example, when heart disease, lung disease, renal disease are caused by smoking.

However, cohort studies are major undertakings. They may require long periods of follow-up since disease may occur a long time after exposure. Therefore, it is a very expensive study design.

Cohort studies work well for rare exposures –you can specifically select people exposed to a certain factor. But this design does not work for rare diseases –you would then need a large study group to find sufficient disease cases.

Case-control studies are relatively simple to conduct. They do not require a long follow-up period (as the disease has already developed), and are hence much cheaper. This design is especially useful for rare diseases (as you select the cases yourself), but not for rare causes (as you will probably not find these in sufficient number in your study). It is also very suitable for diseases with a long latent period, such as cancer.

However, case-control studies are less adept at showing a causal relationship than cohort studies. They are more prone to bias.

One example is recall bias: cases might recall certain exposures more clearly than controls, simply due to the fact that they have thought about what could have caused their disease.

by Annette Gerritsen, Ph.D.

About the Author: With expertise in epidemiology, biostatistics and quantitative research projects, Annette Gerritsen, Ph.D. provides s ervices to her clients focussing on the methodological soundness of each phase of an epidemiological study to ensure getting valid answers to the proposed research questions. She is the founder of Epi Result .

Reader Interactions

December 2, 2020 at 2:18 pm

Thank a a lot for simple explaining it clearly.

March 31, 2020 at 2:47 am

March 24, 2020 at 11:49 pm

what would be best study design to find out causal relationship between lung cancer and smoking?

February 4, 2019 at 8:32 pm

Super clear love it I’m only in eighth grade. I’m doing science Olympiad and this really helped me for disease detectives!!

June 5, 2018 at 1:17 am

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March 21, 2018 at 5:12 am

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November 14, 2017 at 8:44 pm

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May 10, 2017 at 10:45 am

the article is well simplified for easy understanding. Thanks.

July 14, 2014 at 1:49 pm

Well written, simple and clear explanation. I m no longer confused now! Thanks

November 4, 2013 at 1:53 am

Dear Annette Gerritsen, could you please explain the difference of interpretation between RR in cohort study and OR in case control study? I have read some books but it’s still not clear.

Best regards,

Sarumpaet Sori Muda

September 14, 2013 at 4:24 am

The article is clear,well written and easy to understand

April 18, 2013 at 11:22 pm

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April 16, 2013 at 2:50 pm

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February 13, 2013 at 5:37 am

The article is clear. However, I would like you to help me on the methodology of a study that I would like to do.

I would like to do an intervention whereby I was thinking that data in the registers before the intervention would be used as a baseline and after the intervention would compare the treatment outcomes for baseline and end-line.

May you help me with how best can I can put this idea on paper?

December 21, 2012 at 8:09 pm

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October 14, 2012 at 7:14 pm

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Privacy Overview

Case-control matching: effects, misconceptions, and recommendations

Affiliations.

1 Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, PO Box 14155-6446, Tehran, Iran. [email protected].
2 Division of Biostatistics, School of Public Health, University of California, Berkeley, CA, USA.
3 Department of Statistics, University of California, Berkeley, CA, USA.
4 Department of Epidemiology, Fielding School of Public Health, University of California, Los Angeles, CA, USA.
5 Department of Statistics, College of Letters and Science, University of California, Los Angeles, CA, USA.
PMID: 29101596
PMCID: PMC9188848
DOI: 10.1007/s10654-017-0325-0

Misconceptions about the impact of case-control matching remain common. We discuss several subtle problems associated with matched case-control studies that do not arise or are minor in matched cohort studies: (1) matching, even for non-confounders, can create selection bias; (2) matching distorts dose-response relations between matching variables and the outcome; (3) unbiased estimation requires accounting for the actual matching protocol as well as for any residual confounding effects; (4) for efficiency, identically matched groups should be collapsed; (5) matching may harm precision and power; (6) matched analyses may suffer from sparse-data bias, even when using basic sparse-data methods. These problems support advice to limit case-control matching to a few strong well-measured confounders, which would devolve to no matching if no such confounders are measured. On the positive side, odds ratio modification by matched variables can be assessed in matched case-control studies without further data, and when one knows either the distribution of the matching factors or their relation to the outcome in the source population, one can estimate and study patterns in absolute rates. Throughout, we emphasize distinctions from the more intuitive impacts of cohort matching.

Keywords: Bias; Case–control studies; Confounding; Matching; Odds ratio.

Case-Control Studies*
Cohort Studies
Confounding Factors, Epidemiologic*
Matched-Pair Analysis*
Research Design

Grants and funding

R56 AI134724/AI/NIAID NIH HHS/United States

Case Report vs Case-Control Study: A Simple Explanation

A case report is the description of the clinical story of a single patient, whereas a case-control study compares 2 groups of participants differing in outcome in order to determine if a suspected exposure in their past caused that difference.

Here’s the evidence pyramid showing the level of evidence for different study designs:

Pyramid representing the levels of evidence for each study design

Cohort Studies

Advantages & Disadvantages of Cohort Studies

Disadvantages of prospective cohort studies, disadvantages of retrospective cohort studies.

Epi_Tools.XLSX

All Modules

Clarity of Temporal Sequence (Did the exposure precede the outcome?): Cohort studies more clearly indicate the temporal sequence between exposure and outcome, because in a cohort study, subjects are known to be disease-free at the beginning of the observation period when their exposure status is established. In case-control studies, one begins with diseased and non-diseased people and then acertains their prior exposures. This is a reasonable approach to establishing past exposures, but subjects may have difficulty remembering past exposures, and their recollection may be biased by having the outcome (recall bias).

Allow Calculation of Incidence: Cohort studies allow you to calculate the incidence of disease in exposure groups, so you can calculate:

Absolute risk (incidence)
Relative risk (risk ratio or rate ratio)
Risk difference
Attributable proportion (attributable risk %)

Facilitate Study of Rare Exposures: While a cohort design can be used to investigate common exposures (e.g., risk factors for cardiovascular disease and cancer in the Nurses' Health Study), they are particularly useful for evaluating the effects of rare or unusual exposures, because the investigators can make it a point to identify an adequate number of subjects who have an unusual exposure, e.g.,

Exposure to toxic chemicals (Agent Orange)
Adverse effects of drugs (e.g., thalidomide) or treatments (e.g., radiation treatments for ankylosing spondylitis)
Unusual occupational exposures (e.g., asbestos, or solvents in tire manufacturing, )

Allow Examination of Multiple Effects of a Single Exposure

One could look at the association between exposure to Agent Orange and several different outcomes.

Avoid Selection Bias at Enrollment: Cohort studies, especially prospective cohort studies, reduce the possibility that the results will be biased by selecting subjects for the comparison group who may be more or less likely to have the outcome of interest, because in a cohort study the outcome is not known at baseline when exposure status is established. Nevertheless, selection bias can occur in retrospective cohort studies (since the outcomes have already occurred at the time of selection), and it can occur in prospective cohort studies as a result of differential loss to follow up.

The "Air Force Health Study" on agent orange illustrates these advantages.

It was clear that the exposure preceded adverse outcomes among exposed subjects who developed problems.
It was used to evaluate the effects of an unusual risk factors (agent orange).
It allowed direct calculation of incidence rates.
It enabled the investigators to study multiple outcomes of this single unusual exposure.
The prospective component of the study was not biased by knowledge of outcome status, because the outcomes hadn't occurred at the time of enrollment.

Link to a v ideo on Agent Orange from the the New York Times

You may have to follow large numbers of subjects for a long time.
They can be very expensive and time consuming.
They are not good for rare diseases.
They are not good for diseases with a long latency.
Differential loss to follow up can introduce bias.
As with prospective cohort studies, they are not good for very rare diseases.
If one uses records that were not designed for the study, the available data may be of poor quality.
There is frequently an absence of data on potential confounding factors if the data was recorded in the past.
It may be difficult to identify an appropriate exposed cohort and an appropriate comparison group.
Differential losses to follow up can also bias retrospective cohort studies.

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Case-control studies:...

Case-control studies: advantages and disadvantages

Related content
Peer review
Philip Sedgwick , reader in medical statistics and medical education 1
1 Centre for Medical and Healthcare Education, St George’s, University of London, London, UK
p.sedgwick{at}sgul.ac.uk

Researchers investigated the risk factors associated with the development of pulmonary tuberculosis in Russia. A case-control study was performed in the city of Samara, 700 miles south east of Moscow. Cases were 334 consecutive adults diagnosed as having culture confirmed pulmonary tuberculosis at any of the city’s specialist tuberculosis clinics between 1 January 2003 and 31 December 2003. For each case, a control matched for year of birth and sex, and with no history of tuberculosis, was sampled randomly from a registry of the general population of Samara city. A questionnaire was used to collect information retrospectively about potential risk factors before and during the development of pulmonary tuberculosis. Controls were asked about exposure to risk factors before the index date for their matched case—that is, the date when tuberculosis was diagnosed. 1

The researchers reported that the most important risk factors associated with the development of pulmonary tuberculosis were raw milk and unemployment.

Which of the following statements, if any, are true?

a) The sampling of the controls was prone to selection bias

b) The information collected by the questionnaire was prone to recall bias

c) It was possible to estimate the population at risk of pulmonary tuberculosis

d) It can be inferred that raw milk and unemployment cause pulmonary tuberculosis

Statement b is true, whereas a , c, and d are false.

The purpose of the study was to establish those risk factors associated with the development of pulmonary tuberculosis. A case-control study design was used. Two groups of people were identified on the basis of their health status—those with tuberculosis (the cases) and otherwise healthy people with no history of pulmonary tuberculosis (the controls). A case-control study is retrospective in design. In the example above, information about past exposure to potential risk factors before and during the development of pulmonary tuberculosis was collected by questionnaire. The cases and controls were compared to ascertain whether particular risk factors were more common in one group than in the other.

It is important that consideration is given to the selection of controls for a case-control study. Typically, the controls will have had no history of the disease or condition of interest. Furthermore, the controls should be representative of the population. However, controls are often recruited through convenience sampling—for example, from a hospital clinic or a general practice. Therefore, any resulting sample of controls would not be representative of the general population in terms of health. Hence, the recruitment of controls is typically prone to selection bias—that is, the controls are systematically different from the population they are meant to represent. Any observed differences between cases and controls in the measured risk factors would not reflect that in the general population. In the example above, the controls were selected at random from a registry of the general population of Samara city. Because the controls were selected at random, they would not be prone to selection bias ( a is false). Case and controls were matched for year of birth and sex. The advantages of matching in case-control studies have been described in a previous question. 2

A questionnaire was used to collect information about past exposure to a variety of risk factors. The study was retrospective in design, with participants reporting exposure to risk factors before and during the development of pulmonary tuberculosis. Therefore, the information recorded would have been prone to recall bias ( b is true). Recall bias, described in a previous question, 3 is the systematic difference between the cases and controls in the accuracy of reported information about past exposure to risk factors. Recall bias will be present if participants have selective preconceptions about the association between pulmonary tuberculosis and past exposure to the risk factor(s).

Relative risk would have been the preferred measure of the association between pulmonary tuberculosis and each recorded risk factor. 4 However, in the example above it was not possible to calculate the relative risk of pulmonary tuberculosis for those with a particular risk factor present relative to those without. This is because it is not possible to estimate directly the population at risk in a case-control study ( c is false), as described in a previous question. 5 Estimating the population at risk would involve estimating the incidence or prevalence of pulmonary tuberculosis in the population. This would be not only for the entire population, but also those with and without each risk factor present. For a case-control study the odds ratio can be derived instead as an estimate of the relative risk. Odds and odds ratios have been described previously. 6 Adjusted odds ratios were derived in the example above—that is, confounding was adjusted for to allow for the simultaneous effects of other variables studied. Odds ratios can be adjusted for confounding using a statistical method known as logistic regression. 7

The researchers reported that development of pulmonary tuberculosis was associated with exposure to the risk factors of raw milk and unemployment. However, it cannot be inferred that raw milk or unemployment causes pulmonary tuberculosis ( d is false), only that those people who had drunk raw milk or who had been unemployed were more likely to have developed pulmonary tuberculosis. This is because it is not always possible in case-control studies to predict whether exposure to the risk factors preceded development of the disease or condition. Furthermore, it was not possible to measure and then control for, through statistical analysis, all factors that may have affected the development of pulmonary tuberculosis. The observed associations between pulmonary tuberculosis and raw milk plus unemployment may have been the result of confounding—other risk factors that were not measured may have been associated with raw milk or unemployment and been the cause of pulmonary tuberculosis. Only an association between a risk factor and disease or condition, and not causation, can be inferred from the results of a case-control study. This is in contrast to an experimental study, such as a clinical trial, that uses random allocation to control for confounding at baseline.

Case-control studies are observational by design. Other types of observational studies include prospective cohort studies. 8 An observational study is one in which researchers do not intervene in any way but simply observe and record people’s behaviour, symptoms, attitudes, or other characteristics.

Case-control studies are generally quick, cheap, and easy to perform. Cases and controls are often sampled from, for example, an existing database of health records on a group of patients. Furthermore, case-control studies are particularly suitable for studying risk factors associated with rare diseases or conditions. In contrast, an observational design such as a prospective cohort study would not be suitable if the disease or condition is rare because it is unlikely that many members of a cohort will develop the disease or condition of interest. Case-control studies are not prone to loss to follow-up, unlike cohort studies. Sometimes case-control studies are performed as initial studies to establish potential associations before undertaking larger and more expensive studies. A disadvantage of case-control studies, in addition to those described above, is that they are not suitable when exposure to any of the risk factors is rare because few, if any, of the cases or controls are likely to have been exposed to them.

Cite this as: BMJ 2014;348:f7707

Competing interests: None declared.

↵ Coker R, McKee M, Atun R, Dimitrova B, Dodonova E, Kuznetsov S, et al. Risk factors for pulmonary tuberculosis in Russia: case-control study. BMJ 2006 ; 332 : 85 . OpenUrl Abstract / FREE Full Text
↵ Sedgwick P. Why match in case-control studies? BMJ 2012 ; 344 : e691 . OpenUrl FREE Full Text
↵ Sedgwick P. What is recall bias? BMJ 2012 ; 344 : e3519 . OpenUrl FREE Full Text
↵ Sedgwick P, Marston L. Relative risks. BMJ 2010 ; 341 : c3983 . OpenUrl FREE Full Text
↵ Sedgwick P. Case-control studies: measures of risk. BMJ 2013 ; 346 : f1185 . OpenUrl FREE Full Text
↵ Sedgwick P. Odds and odds ratios. BMJ 2013 ; 347 : f5067 . OpenUrl FREE Full Text
↵ Sedgwick P. Logistic regression. BMJ 2013 ; 347 : f4488 . OpenUrl FREE Full Text
↵ Sedgwick P. Prospective cohort studies: advantages and disadvantages. BMJ 2013 ; 347 : f6726 . OpenUrl FREE Full Text

Epidemiology: Advantages and Disadvantages of Case Control Studies

Easy to conduct as no follow up is required
No attrition, as no follow up is needed
Gives faster results
Inexpensive
Suitable for rare and newly identified diseases
More than one risk factors can be studied simultaneously
Ethical problem lesser as disease has already occurred

Disadvantages

The study depends upon the history given by the subject. Hence recall bias can occur
Sometimes finding and appropriate control is difficult
We can only establish an association between the disease and the risk factor as we don’t know what occurred first
We can’t know the incidence hence can’t calculate the relative risk (RR) directly from a case-control study

Examples of case-control studies:

Study of Reye’s syndrome
Study of adenocarcinoma of vagina
Oral contraceptives and thrombo-embolic phenomenon
Thalidomide tragedy

References: Tiwari P. Epidemiology Made Easy. New Delhi: Jaypee Brothers; 2003 Gordis, L. (2014). Epidemiology (Fifth edition.). Philadelphia, PA: Elsevier Saunders. Bonita, R., Beaglehole, R., Kjellström, T., & World Health Organization. (2006). Basic epidemiology. Geneva: World Health Organization. Schneider, Dona, Lilienfeld, David E (Eds.), 4th ed. Lilienfeld’s Foundations of Epidemiology. New York: Oxford University Press; 2015 K. Park. Principles of Epidemiology and Epidemiologic Methods. In Park’s Textbook of Preventive and Social Medicine. 24th Ed. Jabalpur: Banarasidas Bhanot, 2017: pg 58 – 145

(adsbygoogle = window.adsbygoogle || []).push({ google_ad_client: "ca-pub-2296304216845141", enable_page_level_ads: true }); Steps in Investigation of an Epidemic: http://www.ihatepsm.com/blog/epidemiology-steps-investigation-epidemic Tests of Significance : http://www.ihatepsm.com/blog/epidemiology-tests-significance Monitoring and Evaluation : http://www.ihatepsm.com/blog/epidemiology-monitoring-and-evaluation Advantages and Disadvantages of Case Control Studies : http://www.ihatepsm.com/blog/epidemiology-advantages-and-disadvantages-c... Advantages and disadvantages of cohort study : http://www.ihatepsm.com/blog/epidemiology-advantages-and-disadvantages-c... Basic Concepts in Epidemiology: http://www.ihatepsm.com/blog/epidemiology-basic-concepts Types of Epidemiological Studies: http://www.ihatepsm.com/blog/epidemiology-types-epidemiological-studies Differences between Case – control and cohort study : http://www.ihatepsm.com/blog/epidemiology-differences-between-case-%E2%8... Uses of epidemiology: http://www.ihatepsm.com/blog/epidemiology-uses-epidemiology Blinding in Experimental Studies: http://www.ihatepsm.com/blog/blinding-experimental-studies Evaluation of a Screening Test : http://www.ihatepsm.com/blog/epidemiology-evaluation-screening-test

10 Case Study Advantages and Disadvantages

case study advantages and disadvantages, explained below

A case study in academic research is a detailed and in-depth examination of a specific instance or event, generally conducted through a qualitative approach to data.

The most common case study definition that I come across is is Robert K. Yin’s (2003, p. 13) quote provided below:

“An empirical inquiry that investigates a contemporary phenomenon within its real-life context, especially when the boundaries between phenomenon and context are not clearly evident.”

Researchers conduct case studies for a number of reasons, such as to explore complex phenomena within their real-life context, to look at a particularly interesting instance of a situation, or to dig deeper into something of interest identified in a wider-scale project.

While case studies render extremely interesting data, they have many limitations and are not suitable for all studies. One key limitation is that a case study’s findings are not usually generalizable to broader populations because one instance cannot be used to infer trends across populations.

Case Study Advantages and Disadvantages

1. in-depth analysis of complex phenomena.

Case study design allows researchers to delve deeply into intricate issues and situations.

By focusing on a specific instance or event, researchers can uncover nuanced details and layers of understanding that might be missed with other research methods, especially large-scale survey studies.

As Lee and Saunders (2017) argue,

“It allows that particular event to be studies in detail so that its unique qualities may be identified.”

This depth of analysis can provide rich insights into the underlying factors and dynamics of the studied phenomenon.

2. Holistic Understanding

Building on the above point, case studies can help us to understand a topic holistically and from multiple angles.

This means the researcher isn’t restricted to just examining a topic by using a pre-determined set of questions, as with questionnaires. Instead, researchers can use qualitative methods to delve into the many different angles, perspectives, and contextual factors related to the case study.

We can turn to Lee and Saunders (2017) again, who notes that case study researchers “develop a deep, holistic understanding of a particular phenomenon” with the intent of deeply understanding the phenomenon.

3. Examination of rare and Unusual Phenomena

We need to use case study methods when we stumble upon “rare and unusual” (Lee & Saunders, 2017) phenomena that would tend to be seen as mere outliers in population studies.

Take, for example, a child genius. A population study of all children of that child’s age would merely see this child as an outlier in the dataset, and this child may even be removed in order to predict overall trends.

So, to truly come to an understanding of this child and get insights into the environmental conditions that led to this child’s remarkable cognitive development, we need to do an in-depth study of this child specifically – so, we’d use a case study.

4. Helps Reveal the Experiences of Marginalzied Groups

Just as rare and unsual cases can be overlooked in population studies, so too can the experiences, beliefs, and perspectives of marginalized groups.

As Lee and Saunders (2017) argue, “case studies are also extremely useful in helping the expression of the voices of people whose interests are often ignored.”

Take, for example, the experiences of minority populations as they navigate healthcare systems. This was for many years a “hidden” phenomenon, not examined by researchers. It took case study designs to truly reveal this phenomenon, which helped to raise practitioners’ awareness of the importance of cultural sensitivity in medicine.

5. Ideal in Situations where Researchers cannot Control the Variables

Experimental designs – where a study takes place in a lab or controlled environment – are excellent for determining cause and effect . But not all studies can take place in controlled environments (Tetnowski, 2015).

When we’re out in the field doing observational studies or similar fieldwork, we don’t have the freedom to isolate dependent and independent variables. We need to use alternate methods.

Case studies are ideal in such situations.

A case study design will allow researchers to deeply immerse themselves in a setting (potentially combining it with methods such as ethnography or researcher observation) in order to see how phenomena take place in real-life settings.

6. Supports the generation of new theories or hypotheses

While large-scale quantitative studies such as cross-sectional designs and population surveys are excellent at testing theories and hypotheses on a large scale, they need a hypothesis to start off with!

This is where case studies – in the form of grounded research – come in. Often, a case study doesn’t start with a hypothesis. Instead, it ends with a hypothesis based upon the findings within a singular setting.

The deep analysis allows for hypotheses to emerge, which can then be taken to larger-scale studies in order to conduct further, more generalizable, testing of the hypothesis or theory.

7. Reveals the Unexpected

When a largescale quantitative research project has a clear hypothesis that it will test, it often becomes very rigid and has tunnel-vision on just exploring the hypothesis.

Of course, a structured scientific examination of the effects of specific interventions targeted at specific variables is extermely valuable.

But narrowly-focused studies often fail to shine a spotlight on unexpected and emergent data. Here, case studies come in very useful. Oftentimes, researchers set their eyes on a phenomenon and, when examining it closely with case studies, identify data and come to conclusions that are unprecedented, unforeseen, and outright surprising.

As Lars Meier (2009, p. 975) marvels, “where else can we become a part of foreign social worlds and have the chance to become aware of the unexpected?”

Disadvantages

1. not usually generalizable.

Case studies are not generalizable because they tend not to look at a broad enough corpus of data to be able to infer that there is a trend across a population.

As Yang (2022) argues, “by definition, case studies can make no claims to be typical.”

Case studies focus on one specific instance of a phenomenon. They explore the context, nuances, and situational factors that have come to bear on the case study. This is really useful for bringing to light important, new, and surprising information, as I’ve already covered.

But , it’s not often useful for generating data that has validity beyond the specific case study being examined.

2. Subjectivity in interpretation

Case studies usually (but not always) use qualitative data which helps to get deep into a topic and explain it in human terms, finding insights unattainable by quantitative data.

But qualitative data in case studies relies heavily on researcher interpretation. While researchers can be trained and work hard to focus on minimizing subjectivity (through methods like triangulation), it often emerges – some might argue it’s innevitable in qualitative studies.

So, a criticism of case studies could be that they’re more prone to subjectivity – and researchers need to take strides to address this in their studies.

3. Difficulty in replicating results

Case study research is often non-replicable because the study takes place in complex real-world settings where variables are not controlled.

So, when returning to a setting to re-do or attempt to replicate a study, we often find that the variables have changed to such an extent that replication is difficult. Furthermore, new researchers (with new subjective eyes) may catch things that the other readers overlooked.

Replication is even harder when researchers attempt to replicate a case study design in a new setting or with different participants.

Comprehension Quiz for Students

Question 1: What benefit do case studies offer when exploring the experiences of marginalized groups?

a) They provide generalizable data. b) They help express the voices of often-ignored individuals. c) They control all variables for the study. d) They always start with a clear hypothesis.

Question 2: Why might case studies be considered ideal for situations where researchers cannot control all variables?

a) They provide a structured scientific examination. b) They allow for generalizability across populations. c) They focus on one specific instance of a phenomenon. d) They allow for deep immersion in real-life settings.

Question 3: What is a primary disadvantage of case studies in terms of data applicability?

a) They always focus on the unexpected. b) They are not usually generalizable. c) They support the generation of new theories. d) They provide a holistic understanding.

Question 4: Why might case studies be considered more prone to subjectivity?

a) They always use quantitative data. b) They heavily rely on researcher interpretation, especially with qualitative data. c) They are always replicable. d) They look at a broad corpus of data.

Question 5: In what situations are experimental designs, such as those conducted in labs, most valuable?

a) When there’s a need to study rare and unusual phenomena. b) When a holistic understanding is required. c) When determining cause-and-effect relationships. d) When the study focuses on marginalized groups.

Question 6: Why is replication challenging in case study research?

a) Because they always use qualitative data. b) Because they tend to focus on a broad corpus of data. c) Due to the changing variables in complex real-world settings. d) Because they always start with a hypothesis.

Lee, B., & Saunders, M. N. K. (2017). Conducting Case Study Research for Business and Management Students. SAGE Publications.

Meir, L. (2009). Feasting on the Benefits of Case Study Research. In Mills, A. J., Wiebe, E., & Durepos, G. (Eds.). Encyclopedia of Case Study Research (Vol. 2). London: SAGE Publications.

Tetnowski, J. (2015). Qualitative case study research design. Perspectives on fluency and fluency disorders , 25 (1), 39-45. ( Source )

Yang, S. L. (2022). The War on Corruption in China: Local Reform and Innovation . Taylor & Francis.

Yin, R. (2003). Case Study research. Thousand Oaks, CA: Sage.

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Cohort Study vs Case-Control: Pros, Cons, and Differences

Case-control studies.

A case-control study is a kind of research design in which two subsisting groups varying in outcome are differentiated and classified on the basis of some conjectured casual characteristic.

Case-control research subjects chosen based on disease status and assessed for previous illness to a risk factor of interest. “Cases” are those determined to have the disease or outcome of interest. “Controls” are free from the disease or outcome of interest. The illness data can come from a variety of sources, like subsisting data in a medical record or by surveying the participants.You can think of this as a “flashback” study.

It accounts for the passage of time using a flashback technique to assess past characteristics or exposures and to groups of people, cases and controls.

Case-Control Study Design

At the present time, cases and controls are identified and past exposures are measured. The study determines the odds of having the exposure among cases and controls, and then compares these groups to determine the association between the exposure and the outcome. Unbiased selection of cases and controls is very crucial to this research design. Selection biases pose a substantial threat to the validity of study findings for this design.Case-control studies measure the probabilities of having an exposure or characteristics in the case and control populations.

These probabilities are then compared using the odds ratio – a measure of association.

Cohort Study

A cohort study, in other words a prospective study, is a research design which study subjects – disease-free at enrollment and chosen based exposure rank. Unexposed and exposed groups are followed for the same amount of time to determine who develops the disease of interest. This design meets the need to confirm a casual relation between exposure and outcome. Since the exposure is established first and the potential effect is captured prospectively, the temporal association is actually witnessed during the course of the study.You can think of it as a “motion picture” study.

This research design follows groups of individuals free from disease through a period of time in motion picture fashion to determine whether the disease develops.

Cohort Study Design

A cohort time at present time exposed and unexposed are recruited to be in the study, then they are followed prospectively to see if they amplify the outcome or disease of interest. The exposed and unexposed groups are then compared to determine the association with the outcome. Data from cohort studies are used to calculate the risk or rate of the health characteristics or disease.

Pros and Cons

Strengths of Case-Control Studies:

Can be relatively quickly done;
Can be relatively inexpensive;
Good for diseases with short or long latency/duration;
Good for rare diseases.

Weaknesses of Case-Control Studies:

Inefficient for rare exposures;
Usually unable to determine prevalence or the incidence in the population;
More predisposed to bias, especially selection, and recall biases.

Strengths of Cohort Studies:

Good for rare exposures;
Exposure clearly precedes disease;
Can examine multiple effects of an exposure;
Able to determine incidence of disease in population.

Weaknesses of Cohort Studies:

Can be expansive;
Inefficient for rare diseases;
Once begun, difficult to examine other study factors (exposures);
The prospective study may take a long time if the disease has a long latency.

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What is a case-control study and how does it differ (or how is it...

What is a case-control study and how does it differ (or how is it the same) as the cohort study design?
Can you talk about the ways bias shows up in a study design (such as, selection bias) etc.?
What is different in a randomized control trial than, for instance, a case-control study (or a cohort study)? What does it mean to show a causal relationship?
What is each type of study used for, its purpose, and its outcomes? How are the outcomes different in each study design? Measured?
What is an intervention group? Where is it found?
Can you explain a retrospective versus a prospective study design? What are the pros and cons of each?
How are groups selected for each of the study designs?
What is meant by "scientific misconduct"?
Differentiate: random error, systematic error, confounding error.

Answer & Explanation

1) A case-control study is an observational study design where existing cases of a disease are compared to existing controls without the disease. It is different from a cohort study design in that the case-control typically starts with the outcome of interest and traces back to the exposure, whereas in a cohort study, people are followed and observed over time.

2) Bias can show up in a study design in several ways, including selection bias (when the chosen sample is not representative of the target population), sampling bias (when the chosen sample is not random), measurement bias (when measurements are not taken accurately), and recall bias (when participants are asked to recall past events).

3) A randomized control trial differs from a case-control study or cohort study in that it is an experiment where participants are randomly assigned to either a control or an intervention group. This allows for the determination of a causal relationship between the exposure and the outcome.

4) Case-control studies are used to investigate the relationship between an exposure and an outcome that has already occurred; cohort studies are used to investigate the relationship between an exposure and an outcome over time; and randomized control trials are used to test the efficacy and safety of an intervention. Outcomes are typically measured differently depending on the study design.

5) An intervention group is a group of participants in a study who are assigned to receive the intervention being tested. It can be found in randomized control trials, where one group of participants is assigned to receive the intervention and the other group acts as the control.

6) A retrospective study design looks back in time, while a prospective study design looks forward. Retrospective studies are less expensive and time-consuming, but they are more prone to bias than prospective studies. Prospective studies are more expensive and time-consuming, but they are less prone to bias than retrospective studies.

7) Groups for each study design are typically selected based on predetermined criteria, such as certain demographics or risk factors. For example, in a case-control study, cases and controls are typically chosen based on having or not having a certain disease.

8) Scientific misconduct is any intentional or unintentional action that leads to inaccurate or misleading research results. Examples include fabrication of data, plagiarism, and failure to disclose conflicts of interest.

9) Random error is random variation in the measurement of a variable due to chance. Systematic error is a bias or consistent deviation from the true value due to some systematic cause, such as faulty instruments or poor technique. Confounding error is when the effect of one factor is confounded with the effect of another factor.

Introduction

Medical research is an important tool in aiding the understanding of the etiology, diagnosis, and treatment of various diseases and conditions. However, there are different types of research designs used to study these medical topics, each with their own strengths and weaknesses. This paper will discuss the different types of study designs used in medical research, such as case-control, cohort, randomized control trials, retrospective, and prospective studies. In addition, it will discuss the ways bias can appear in study designs and the ways in which groups are selected for each type of study. Finally, the paper will explain the different types of errors that can occur in medical research, as well as what is meant by scientific misconduct.

Case-Control Studies

Case-control studies are a type of observational study design, where existing cases of a disease are compared to existing controls without the disease (Schober & Vetter, 2021). The purpose of case-control studies is to investigate the relationship between an exposure and an outcome that has already occurred. The cases and controls are typically chosen based on having or not having the disease or outcome of interest. This type of study design starts with the outcome of interest and traces back to the exposure, instead of following people over time like a cohort study.

Cohort Studies

Cohort studies are another type of observational study design, where a group of people are followed over time and observed for changes in the outcome of interest. The purpose of cohort studies is to investigate the relationship between an exposure and an outcome over time. This type of study design typically starts with the exposure and follows the people over time, instead of starting with the outcome like a case-control study.

Randomized Control Trials

Randomized control trials (RCTs) are a type of experiment, where participants are randomly assigned to either a control or an intervention group. The purpose of RCTs is to test the efficacy and safety of an intervention. This type of study design allows for the determination of a causal relationship betwe

en the exposure and the outcome, which is not possible with observational study designs.

Retrospective and Prospective Studies

Retrospective studies are a type of observational study design, where data is collected looking back in time. The purpose of retrospective studies is to investigate the relationship between an exposure and an outcome that has already occurred. This type of study design is less expensive and time-consuming, but it is more prone to bias than prospective studies.

Prospective studies are another type of observational study design, where data is collected looking forward in time. The purpose of prospective studies is to investigate the relationship between an exposure and an outcome over time. This type of study design is more expensive and time-consuming, but it is less prone to bias than retrospective studies.

Bias can show up in a study design in several ways, including selection bias (when the chosen sample is not representative of the target population), sampling bias (when the chosen sample is not random), measurement bias (when measurements are not taken accurately), and recall bias (when participants are asked to recall past events) (Schober & Vetter, 2021). Bias can lead to inaccurate and misleading research results, so it is important to recognize and address potential sources of bias in a study design.

Groups for each study design are typically selected based on predetermined criteria, such as certain demographics or risk factors. For example, in a case-control study, cases and controls are typically chosen based on having or not having a certain disease. In a cohort study, people are typically chosen based on their exposure status or risk factors. In a randomized control trial, people are randomly assigned to either a control or an intervention group.

In medical research, there are three types of errors that can occur: random error, systematic error, and confounding error. Random error is random variation in the measurement of a variable due to chance. Systematic error is a bias or consistent deviation from the true value due to some systematic cause, such as faulty instruments or poor technique (Ioannidis, et al., 2021). Confounding error is when the effect of one factor is confounded with the effect of another factor.

Scientific Misconduct

Scientific misconduct is any intentional or unintentional action that leads to inaccurate or misleading research results. Examples include fabrication of data, plagiarism, and failure to disclose conflicts of interest (Ioannidis, et al., 2021). It is important for researchers to adhere to ethical standards and strive for accuracy and honesty in their research.

Medical research is an important tool for understanding the etiology, diagnosis, and treatment of various diseases and conditions. There are different types of study designs used in medical research, each with their own strengths and weaknesses. This paper discussed the different types of study designs used in medical research, such as case-control, cohort, randomized control trials, retrospective, and prospective studies. In addition, it discussed the ways bias can appear in study designs and the ways in which groups are selected for each type of study. Finally, the paper explained the different types of errors that can occur in medical research, as well as what is meant by scientific misconduct. By understanding the different types of study designs, bias, and errors, researchers can strive for accuracy and honesty in their research.

Ioannidis, J. P., Powe, N. R., & Yancy, C. (2021). Recalibrating the use of race in medical research. Jama, 325(7), 623-624.

Schober, P., & Vetter, T. R. (2021). Logistic regression in medical research. Anesthesia and analgesia, 132(2), 365.

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Pros and Cons of case studies

Post author: admin
Post published: March 27, 2020
Post category: Education
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Case studies are research methodologies that are used and analyzed in order to depict principles; they have been usually used in social sciences. They are research strategies and experiential inquiries that seek to examine various phenomena within a real-life context. Case studies seek to explain and give details in the analysis of people and events. There are several pros that back case studies and there are cons too that criticize them. The pros and cons are listed below.

1 . They show client observations- Since case studies are strategies that are used and analyzed in order to describe principles therefore it seeks to show indeed the client investigated and experienced a particular phenomenon.

2 . Makes practical improvements- Case studies present facts that categorically describe particular people or events in order to make some of the necessary improvements. Case studies data is what supports a particular belief.

3 . They are an influential way of portraying something- If a researcher wants to prove a particular principle to be true, he or she must back it by case studies in order to make the other people and the naysayers believe.

4 . They turn opinions into facts- Case studies present real data on a particular phenomenon. Since facts about various things are presented then it can be verified through this kind of data if the information presented is in the positive or negative development of opinion.

5 . It is relevant to all the parties that are involved- Case studies help the researchers in actively focusing on the data collection process and the participants’ knowledge is bettered. At the end of the process, everybody is able to defend his position through facts.

6 . A number of different research methodologies can be used in case the studies- Case study method goes beyond the interview and direct observations. Secondary data can be obtained from various historical sources that can be used to back the method.

7 . Case studies can be done remotely- It is not essential for a researcher to be present in the specific location of the study in order to effectively use the case study method. Other forms of communication come in to cover that gap for the researcher.

8 . It has a very high cost- If you put this research method in comparison to the others, this one seems more expensive because the cost of accessing data is very high.

9 . Readers can access data from this method very easily- The The format in which case studies present their data is very useful to the readers and easily note the outcomes of the same.

10 . Collects data that cannot be collected by another method- The type of data collected by case studies is much richer and greater in-depth than that of the other experimental methods.

1 . Data collected cannot be generalized- The data collected by the case study method was collected from a smaller population it cannot be generalized to the wider population.

2 . Some of the case studies are not scientific- The weakness of the data collected in some of the case studies that are not scientific is that it cannot be generalized.

3 . It is very difficult to draw a definite cause/effect from case studies- The the kind of data that case studies present cannot be used to draw a definite cause-effect relationship.

4 . Case studies concentrate on one experiment- The problem associated with concentrating on one experiment or a specific group of people is that the data presented might contain some kind of bias.

5 . It takes a lot of time to analyze the data- This process takes longer to analyze the data because there is a very large amount of data that must be collected. Participants might take a lot of time in giving answers or giving inaccurate information.

6 . Case studies can be inefficient processes- Sometimes the researchers are not present in the study areas which means they will not be able to notice whether the information provided is accurate or not terming the whole process inefficient.

7 . Case study method can only be effective with a small sample size- If a very large sample size is involved in the case study it is likely for it to become inefficient because the method requires a small sample size to get the data and analyze it.

8 . The method requires a lot of labor in data collection- The researcher is seriously needed in the data collection of this method. They have to be personally involved in order to be able to identify the quality of the data provided.

9 . There are factors that can influence the data- The method of data collection is meant to collect fact-based data but the power to determine what fact is and what is not is the person who is collecting the data.

10 . There is no right answer in case studies- Case studies do not present any specific answer that is right, the problem arises in the validation of solutions because there is more than one way of looking at things.

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Case control studies.

Steven Tenny ; Connor C. Kerndt ; Mary R. Hoffman .

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Last Update: March 27, 2023 .

Introduction

A case-control study is a type of observational study commonly used to look at factors associated with diseases or outcomes. [1] The case-control study starts with a group of cases, which are the individuals who have the outcome of interest. The researcher then tries to construct a second group of individuals called the controls, who are similar to the case individuals but do not have the outcome of interest. The researcher then looks at historical factors to identify if some exposure(s) is/are found more commonly in the cases than the controls. If the exposure is found more commonly in the cases than in the controls, the researcher can hypothesize that the exposure may be linked to the outcome of interest.

For example, a researcher may want to look at the rare cancer Kaposi's sarcoma. The researcher would find a group of individuals with Kaposi's sarcoma (the cases) and compare them to a group of patients who are similar to the cases in most ways but do not have Kaposi's sarcoma (controls). The researcher could then ask about various exposures to see if any exposure is more common in those with Kaposi's sarcoma (the cases) than those without Kaposi's sarcoma (the controls). The researcher might find that those with Kaposi's sarcoma are more likely to have HIV, and thus conclude that HIV may be a risk factor for the development of Kaposi's sarcoma.

There are many advantages to case-control studies. First, the case-control approach allows for the study of rare diseases. If a disease occurs very infrequently, one would have to follow a large group of people for a long period of time to accrue enough incident cases to study. Such use of resources may be impractical, so a case-control study can be useful for identifying current cases and evaluating historical associated factors. For example, if a disease developed in 1 in 1000 people per year (0.001/year) then in ten years one would expect about 10 cases of a disease to exist in a group of 1000 people. If the disease is much rarer, say 1 in 1,000,0000 per year (0.0000001/year) this would require either having to follow 1,000,0000 people for ten years or 1000 people for 1000 years to accrue ten total cases. As it may be impractical to follow 1,000,000 for ten years or to wait 1000 years for recruitment, a case-control study allows for a more feasible approach.

Second, the case-control study design makes it possible to look at multiple risk factors at once. In the example above about Kaposi's sarcoma, the researcher could ask both the cases and controls about exposures to HIV, asbestos, smoking, lead, sunburns, aniline dye, alcohol, herpes, human papillomavirus, or any number of possible exposures to identify those most likely associated with Kaposi's sarcoma.

Case-control studies can also be very helpful when disease outbreaks occur, and potential links and exposures need to be identified. This study mechanism can be commonly seen in food-related disease outbreaks associated with contaminated products, or when rare diseases start to increase in frequency, as has been seen with measles in recent years.

Because of these advantages, case-control studies are commonly used as one of the first studies to build evidence of an association between exposure and an event or disease.

In a case-control study, the investigator can include unequal numbers of cases with controls such as 2:1 or 4:1 to increase the power of the study.

Disadvantages and Limitations

The most commonly cited disadvantage in case-control studies is the potential for recall bias. [2] Recall bias in a case-control study is the increased likelihood that those with the outcome will recall and report exposures compared to those without the outcome. In other words, even if both groups had exactly the same exposures, the participants in the cases group may report the exposure more often than the controls do. Recall bias may lead to concluding that there are associations between exposure and disease that do not, in fact, exist. It is due to subjects' imperfect memories of past exposures. If people with Kaposi's sarcoma are asked about exposure and history (e.g., HIV, asbestos, smoking, lead, sunburn, aniline dye, alcohol, herpes, human papillomavirus), the individuals with the disease are more likely to think harder about these exposures and recall having some of the exposures that the healthy controls.

Case-control studies, due to their typically retrospective nature, can be used to establish a correlation between exposures and outcomes, but cannot establish causation . These studies simply attempt to find correlations between past events and the current state.

When designing a case-control study, the researcher must find an appropriate control group. Ideally, the case group (those with the outcome) and the control group (those without the outcome) will have almost the same characteristics, such as age, gender, overall health status, and other factors. The two groups should have similar histories and live in similar environments. If, for example, our cases of Kaposi's sarcoma came from across the country but our controls were only chosen from a small community in northern latitudes where people rarely go outside or get sunburns, asking about sunburn may not be a valid exposure to investigate. Similarly, if all of the cases of Kaposi's sarcoma were found to come from a small community outside a battery factory with high levels of lead in the environment, then controls from across the country with minimal lead exposure would not provide an appropriate control group. The investigator must put a great deal of effort into creating a proper control group to bolster the strength of the case-control study as well as enhance their ability to find true and valid potential correlations between exposures and disease states.

Similarly, the researcher must recognize the potential for failing to identify confounding variables or exposures, introducing the possibility of confounding bias, which occurs when a variable that is not being accounted for that has a relationship with both the exposure and outcome. This can cause us to accidentally be studying something we are not accounting for but that may be systematically different between the groups.

The major method for analyzing results in case-control studies is the odds ratio (OR). The odds ratio is the odds of having a disease (or outcome) with the exposure versus the odds of having the disease without the exposure. The most straightforward way to calculate the odds ratio is with a 2 by 2 table divided by exposure and disease status (see below). Mathematically we can write the odds ratio as follows.

Odds ratio = [(Number exposed with disease)/(Number exposed without disease) ]/[(Number not exposed to disease)/(Number not exposed without disease) ]

This can be rewritten as:

Odds ratio = [ (Number exposed with disease) x (Number not exposed without disease) ] / [ (Number exposed without disease ) x (Number not exposed with disease) ]

The odds ratio tells us how strongly the exposure is related to the disease state. An odds ratio of greater than one implies the disease is more likely with exposure. An odds ratio of less than one implies the disease is less likely with exposure and thus the exposure may be protective. For example, a patient with a prior heart attack taking a daily aspirin has a decreased odds of having another heart attack (odds ratio less than one). An odds ratio of one implies there is no relation between the exposure and the disease process.

Odds ratios are often confused with Relative Risk (RR), which is a measure of the probability of the disease or outcome in the exposed vs unexposed groups. For very rare conditions, the OR and RR may be very similar, but they are measuring different aspects of the association between outcome and exposure. The OR is used in case-control studies because RR cannot be estimated; whereas in randomized clinical trials, a direct measurement of the development of events in the exposed and unexposed groups can be seen. RR is also used to compare risk in other prospective study designs.

Issues of Concern

The main issues of concern with a case-control study are recall bias, its retrospective nature, the need for a careful collection of measured variables, and the selection of an appropriate control group. [3] These are discussed above in the disadvantages section.

Clinical Significance

A case-control study is a good tool for exploring risk factors for rare diseases or when other study types are not feasible. Many times an investigator will hypothesize a list of possible risk factors for a disease process and will then use a case-control study to see if there are any possible associations between the risk factors and the disease process. The investigator can then use the data from the case-control study to focus on a few of the most likely causative factors and develop additional hypotheses or questions. Then through further exploration, often using other study types (such as cohort studies or randomized clinical studies) the researcher may be able to develop further support for the evidence of the possible association between the exposure and the outcome.

Enhancing Healthcare Team Outcomes

Case-control studies are prevalent in all fields of medicine from nursing and pharmacy to use in public health and surgical patients. Case-control studies are important for each member of the health care team to not only understand their common occurrence in research but because each part of the health care team has parts to contribute to such studies. One of the most important things each party provides is helping identify correct controls for the cases. Matching the controls across a spectrum of factors outside of the elements of interest take input from nurses, pharmacists, social workers, physicians, demographers, and more. Failure for adequate selection of controls can lead to invalid study conclusions and invalidate the entire study.

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2x2 table with calculations for the odds ratio and 95% confidence interval for the odds ratio. Contributed by Steven Tenny MD, MPH, MBA

Disclosure: Steven Tenny declares no relevant financial relationships with ineligible companies.

Disclosure: Connor Kerndt declares no relevant financial relationships with ineligible companies.

Disclosure: Mary Hoffman declares no relevant financial relationships with ineligible companies.

This book is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ( http://creativecommons.org/licenses/by-nc-nd/4.0/ ), which permits others to distribute the work, provided that the article is not altered or used commercially. You are not required to obtain permission to distribute this article, provided that you credit the author and journal.

Cite this Page Tenny S, Kerndt CC, Hoffman MR. Case Control Studies. [Updated 2023 Mar 27]. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023 Jan-.

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