Reading a Case Control Matching Chart Wxyz

Blazon of observational study comparison two existing groups differing in event

Example–control written report versus cohort on a timeline. "OR" stands for "odds ratio" and "RR" stands for "relative risk".

A case–control study (also known every bit case–referent study) is a type of observational study in which two existing groups differing in issue are identified and compared on the basis of some supposed causal attribute. Example–command studies are oftentimes used to identify factors that may contribute to a medical condition by comparing subjects who have that condition/affliction (the "cases") with patients who do not take the condition/disease but are otherwise similar (the "controls").^[1] They require fewer resource but provide less evidence for causal inference than a randomized controlled trial. A example–control study produces merely an odds ratio, which is an inferior measure of strength of association compared to relative risk.

Definition [edit]

The instance–control is a type of epidemiological observational study. An observational written report is a written report in which subjects are not randomized to the exposed or unexposed groups, rather the subjects are observed in order to decide both their exposure and their outcome status and the exposure status is thus not adamant by the researcher.

Porta's Dictionary of Epidemiology defines the case–command study as: an observational epidemiological study of persons with the affliction (or another outcome variable) of involvement and a suitable control group of persons without the disease (comparing group, reference group).^[2] The potential relationship of a suspected hazard factor or an attribute to the disease is examined by comparing the diseased and nondiseased subjects with regard to how frequently the cistron or attribute is present (or, if quantitative, the levels of the attribute) in each of the groups (diseased and nondiseased)."^[two]

For example, in a study trying to show that people who smoke (the attribute) are more probable to exist diagnosed with lung cancer (the outcome), the cases would be persons with lung cancer, the controls would be persons without lung cancer (not necessarily healthy), and some of each group would be smokers. If a larger proportion of the cases smoke than the controls, that suggests, but does not conclusively show, that the hypothesis is valid.

The instance–command report is frequently assorted with accomplice studies, wherein exposed and unexposed subjects are observed until they develop an outcome of involvement.^{[2] [3]}

Control grouping choice [edit]

Controls need not be in good health; inclusion of sick people is sometimes advisable, every bit the control group should stand for those at take a chance of becoming a case.^[iv] Controls should come from the same population as the cases, and their selection should be independent of the exposures of interest.^[5]

Controls tin acquit the same affliction as the experimental grouping, but of another grade/severity, therefore beingness different from the effect of interest. However, because the deviation between the cases and the controls will be smaller, this results in a lower ability to detect an exposure effect.

Every bit with any epidemiological study, greater numbers in the written report volition increment the power of the study. Numbers of cases and controls do not have to be equal. In many situations, information technology is much easier to recruit controls than to discover cases. Increasing the number of controls in a higher place the number of cases, up to a ratio of about 4 to 1, may be a cost-constructive manner to improve the study.^[4]

Prospective vs. retrospective cohort studies [edit]

A prospective study watches for outcomes, such as the development of a illness, during the study catamenia and relates this to other factors such as suspected risk or protection factor(s). The study usually involves taking a cohort of subjects and watching them over a long menstruum. The outcome of interest should be common; otherwise, the number of outcomes observed will be too pocket-size to be statistically meaningful (indistinguishable from those that may take arisen by adventure). All efforts should be fabricated to avoid sources of bias such as the loss of individuals to follow up during the study. Prospective studies usually have fewer potential sources of bias and confounding than retrospective studies.^[6]

A retrospective study, on the other hand, looks backwards and examines exposures to suspected chance or protection factors in relation to an issue that is established at the start of the study. Many valuable case–control studies, such as Lane and Claypon's 1926 investigation of take a chance factors for chest cancer, were retrospective investigations. Nigh sources of error due to misreckoning and bias are more common in retrospective studies than in prospective studies. For this reason, retrospective investigations are ofttimes criticised. If the outcome of involvement is uncommon, withal, the size of prospective investigation required to estimate relative run a risk is ofttimes as well big to be feasible. In retrospective studies the odds ratio provides an approximate of relative hazard. 1 should have special care to avoid sources of bias and misreckoning in retrospective studies.^[vi]

Strengths and weaknesses [edit]

Example–command studies are a relatively inexpensive and frequently used type of epidemiological study that can be carried out by small teams or private researchers in single facilities in a way that more structured experimental studies often cannot exist. They have pointed the manner to a number of of import discoveries and advances. The case–control study design is often used in the report of rare diseases or as a preliminary report where little is known most the association between the risk factor and disease of interest.^[7]

Compared to prospective accomplice studies they tend to be less plush and shorter in duration. In several situations, they take greater statistical power than accomplice studies, which must often wait for a 'sufficient' number of illness events to accrue.

Case–control studies are observational in nature and thus practise not provide the same level of bear witness as randomized controlled trials. The results may exist confounded by other factors, to the extent of giving the opposite answer to better studies. A meta-assay of what was considered 30 high-quality studies concluded that use of a product halved a risk, when in fact the risk was, if annihilation, increased.^{[8] [9]} It may also be more hard to establish the timeline of exposure to disease outcome in the setting of a case–control study than within a prospective cohort study design where the exposure is ascertained prior to following the subjects over time in order to define their outcome status. The near important drawback in example–control studies relates to the difficulty of obtaining reliable data nearly an individual'south exposure condition over fourth dimension. Case–control studies are therefore placed low in the hierarchy of evidence.

Examples [edit]

I of the most significant triumphs of the instance–control written report was the demonstration of the link between tobacco smoking and lung cancer, by Richard Doll and Bradford Hill. They showed a statistically significant association in a large case–control study.^[ten] Opponents argued for many years that this type of study cannot prove causation, but the eventual results of cohort studies confirmed the causal link which the example–command studies suggested,^{[11] [12]} and it is now accepted that tobacco smoking is the cause of about 87% of all lung cancer mortality in the United states.

Analysis [edit]

Case–control studies were initially analyzed by testing whether or not in that location were meaning differences between the proportion of exposed subjects amongst cases and controls.^[thirteen] Subsequently, Cornfield^[fourteen] pointed out that, when the disease outcome of interest is rare, the odds ratio of exposure can be used to approximate the relative adventure (see rare illness supposition). The validity of the odds ratio depends highly on the nature of the illness studied, on the sampling methodology and on the type of follow-up. Although in classical case–command studies, it remains truthful that the odds ratio tin can simply estimate the relative risk in the case of rare diseases, there is a number of other types of studies (instance–cohort, nested case–command, accomplice studies) in which information technology was later shown that the odds ratio of exposure can exist used to estimate the relative chance or the incidence rate ratio of exposure without the need for the rare disease supposition.^{[13] [15] [xvi]}

When the logistic regression model is used to model the case–control data and the odds ratio is of involvement, both the prospective and retrospective likelihood methods will atomic number 82 to identical maximum likelihood estimations for covariate, except for the intercept.^[17] The usual methods of estimating more interpretable parameters than odds ratios -- such every bit risk ratios, levels, and differences -- is biased if applied to example–control information, but special statistical procedures provide easy to employ consequent estimators.^[18]

Impact on longevity and public wellness [edit]

Tetlock and Gardner claimed that the contributions of medical science to increasing homo longevity and public health were negligible, and too frequently negative, until Scottish physician Archie Cochrane was able to convince the medical establishment to adopt randomized control trials after World State of war Two.^[nineteen]

Encounter also [edit]

• Nested case–control study
• Retrospective cohort report
• Prospective cohort report
• Randomized controlled trial

References [edit]

1. ^ "8. Case–control and cross sectional studies" (PDF) . Retrieved v March 2012.
2. ^ ^{a b c} Porta, 1000., ed. (2008). A Lexicon of Epidemiology (fifth ed.). New York: Oxford University Press. ISBN978-0-xix-531450-2.
3. ^ Rothman, One thousand. (2002). Epidemiology: An Introduction . Oxford, England: Oxford University Press. ISBN978-0-19-513554-1.
4. ^ ^{a b} Grimes DA, Schulz KF (2005). "Compared to what? Finding controls for case–control studies". Lancet. 365 (9468): 1429–33. doi:10.1016/S0140-6736(05)66379-9. PMID 15836892. {{cite journal}}: CS1 maint: uses authors parameter (link)
5. ^ Schulz KF, Grimes DA (2002). "Case–control studies: enquiry in reverse". Lancet. 359 (9304): 431–iv. doi:x.1016/S0140-6736(02)07605-5. PMID 11844534. {{cite periodical}}: CS1 maint: uses authors parameter (link)
6. ^ ^{a b} "Prospective, Retrospective, Case–command, Cohort Studies - StatsDirect". www.statsdirect.com . Retrieved four July 2019.
7. ^ Levin KA (2005). "Study blueprint I". Evidence-Based Dentistry. 6 (3): 78–79. doi:10.1038/sj.ebd.6400355. PMID 16184164. {{cite journal}}: CS1 maint: uses authors parameter (link)
8. ^ Lawlor DA, Davey Smith G, Ebrahim S (2004). "Commentary: the hormone replacement-coronary heart disease puzzler: is this the death of observational epidemiology?". Int J Epidemiol. 33 (3): 464–7. doi:10.1093/ije/dyh124. PMID 15166201. {{cite journal}}: CS1 maint: uses authors parameter (link)
9. ^ Ioannidis JP (2005). "Contradicted and initially stronger furnishings in highly cited clinical inquiry". JAMA. 294 (2): 218–28. doi:10.1001/jama.294.2.218. PMID 16014596. {{cite journal}}: CS1 maint: uses authors parameter (link)
10. ^ Doll R, Colina AB (1950). "Smoking and carcinoma of the lung; preliminary study". Br Med J. 2 (4682): 739–48. doi:10.1136/bmj.2.4682.739. PMC2038856. PMID 14772469. {{cite journal}}: CS1 maint: uses authors parameter (link)
11. ^ Doll R, Hill AB (1956). "Lung cancer and other causes of expiry in relation to smoking; a second study on the mortality of British doctors". Br Med J. 2 (5001): 1071–81. doi:10.1136/bmj.2.5001.1071. PMC2035864. PMID 13364389. {{cite journal}}: CS1 maint: uses authors parameter (link)
12. ^ Doll R, Peto R, Boreham J, Sutherland I (2004). "Bloodshed in relation to smoking: 50 years' observations on male person British doctors". BMJ. 328 (7455): 1519. doi:10.1136/bmj.38142.554479.AE. PMC437139. PMID 15213107. {{cite journal}}: CS1 maint: uses authors parameter (link)
13. ^ ^{a b} Rodrigues, L.; Kirkwood, B. R. (1990). "Case–control designs in the study of common diseases: updates on the demise of the rare disease assumption and the option of sampling scheme for controls". Int. J. Epidemiol. 19 (1): 205–xiii. doi:10.1093/ije/19.1.205. PMID 2190942.
14. ^ Greenhouse SW (1982). "Jerome Cornfield'southward contributions to epidemiology". Biometrics. 38 Suppl: 33–45. doi:10.2307/2529852. PMID 7046823. {{cite journal}}: CS1 maint: uses authors parameter (link)
15. ^ Miettinen, O. (1976). "Estimability and estimation in case–referent studies". Am. J. Epidemiol. 103 (2): 226–35. doi:ten.1093/oxfordjournals.aje.a112220. PMID 1251836.
16. ^ Rothman, K. J.; Greenland, S.; Lash, T. Fifty. (2008). Modern Epidemiology (3rd ed.). Wolters Kluwer, Lippincott Williams & Wilkins. ISBN978-0-7817-5564-1.
17. ^ Prentice RL, Pyke R (1979). "Logistic disease incidence models and example–command studies". Biometrika. 66: 403–411.
18. ^ King, Gary; Zeng, Langche (30 May 2002). "Estimating gamble and rate levels, ratios and differences in case–control studies". Statistics in Medicine. 21 (10): 1409–1427. doi:10.1002/sim.1032. ISSN 0277-6715.
19. ^ Philip E. Tetlock; Dan Gardner (2015). Superforecasting: The fine art and science of prediction. Crown Publishing Group. ISBN978-0-8041-3669-3. Wikidata Q21203378. , esp. ch. 2.

Further reading [edit]

• Stolley, Paul D.; Schlesselman, James J. (1982). Instance–control studies: design, comport, assay . Oxford [Oxfordshire]: Oxford University Printing. ISBN0-19-502933-X. (Still a very useful book, and a great place to start, only now a bit out of date.)

External links [edit]

• Wellcome Trust Case Control Consortium

porterhille1980.blogspot.com

Source: https://en.wikipedia.org/wiki/Case%E2%80%93control_study

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