Case Control study

1. Case Control study

2. E Flow diagram Case-control study D E E Observational study D E Time

3. Case control study A type of observational analytical epidemiological investigation in which the subjects are selected on the basis of whether they do (cases) or do not (controls) have a particular disease under study. The groups are compared with respect to the proportion having an exposure or characteristic of interest.

4. Case control study Also called • Case comparison study • Case referent study • Retrospective study • Trohoc study • “fishing expedition”

5. Case control study Control group Out of control group

6. Selection of cases • Newly diagnosed cases are preferred • Considered incidence at time of diagnosis • Diagnostic criteria

7. Methods of Selecting Cases • All incidence cases in defined population over specific time period • All incidence cases seen at certain hospital over specific time period • Cases seeking case from office-based physician • Schools, places of employment, military service

8. Selection of controls Most difficult and most controversial aspect of study design • The control series is intended to provide an estimate of the exposure rate that would be expected to occur in the cases if there was no association between the study disease and exposure • Individuals selected as controls should not only be free of the disease, but should be similar to the cases in regard to the possibility of having past exposure during the time period of risk.

9. Methods of selecting controls • Probability sample of population from which cases come • School rosters, selective service lists, insurance company lists neighbors of cases • Friends, schoolmates, sibling, fellow workers • Persons seeking medical care at the same institution as cases for condition not related to cases’ disease

10. Ascertainment of exposure • Personal interviews • Existing records • Physical measurements and lab tests

11. Exposure • Yes/no • Intensity • Length of exposure

12. Estimates made from case-control studies • Design of case control studies

13. Measure of effect Odds ratio= ratio of 2 odds • Ratio of odds in favor of exposure among cases to odds in favor of exposure among controls.

14. Measure of effect Odds in favor of exposure among cases (a/c) Odds in favor of exposure among controls (b/d) Odds ratio, Ratio of 2 odds a/c ad = b/d bc

15. Case control study- example • To study the association between smoking and lung cancer, therefore people with lung cancer are enrolled to form the case group, and people without lung cancer are identified as controls. Researchers then look back in time to ascertain each person’s exposure status (smoking history), hence the retrospective nature of this study design.

16. Variables used in study of smoking and lung cancer Subject selection Control selection Males and/or females age matched Occupational group healthy individuals Hospitalized cases patients hospitalized for other cancers Autopsy series patients hospitalized for other diseases Total lung cancer deaths in an area deaths from other causes than cancer National sampling lung cancer deaths sampling of general population Methods of interviewing Other variables concurrently studied Mailed questionnaire geographic distribution Personal interviews subjects/relatives occupation Personal interviews controls: professional marital status Personal interviews controls coffee and alcohol consumption other nutritional factors tobacco use history parity Type of smoking war gas exposure Amount and type other pathologic conditions Amount, type and duration hereditary factors Inhalation practices previous respiratory conditions

17. Association between smoking and lung cancer ODDS RATIO = AD/BC = 33*27/55*2 = 8.1 INTERPRETATION: LUNG CANCER CASES 8 TIMES MORE LIKELY TO BE SMOKERS

18. Bias • Systematic (non random) error in a design or conduct of a study and that results in mistaken conclusions regarding the relationship between the exposure and the outcome • Types: • Selection bias • Information bias • Confounding

19. Selection bias • Selection bias occurs when an investigator inadvertently assigns subjects to comparison groups such that they differ with respect to extraneous factors • Distortion that arises from • Procedures used to select subjects • Factors that influences study participation Systematic error in identifying/ selecting subjects

20. Selection bias example • Study- to find the association between CHD and caffeine consumption • Cases- patients undergoing cardiac catheterization at university medical center • Controls- randomly selected patients hospitalized for conditions other than CHD The incidence of peptic ulcers is significantly higher in the control group and hence, their overall caffeine consumption is less than that of the cases. As a result, the controls do not accurately represent the disease-free population and the results of the study are undermined by selection bias.

21. Types of selection bias • Berksonian bias (hospital admission rate bias) • Neyman bias (also called incidence-prevalence bias) • Detection (unmasking) • Non response bias

22. Berkson bias (admission-rate bias) • Results from differential rates of hospital admission for cases and controls • Knowledge of the exposure of interest might lead to an increased rate of admission to hospital Example: Doctors who care for women with salpingitis were more likely to recommend hospital admission for those using an intrauterine device (IUD) than for those using a hormonal method of contraception. In a hospital based case control study, this would stack the deck (or gynaecology ward) with a high proportion of IUD exposed cases, spuriously increasing the odds ratio

23. Neyman bias (incidence-prevalence bias) • Arises when a gap in time occurs between exposure and selection of study participants • Crops up in studies of diseases that are quickly fatal, transient, or subclinical • Creates a case group not representative of cases in the community. Example: A hospital based case control study of myocardial infarction and snow shovelling (the exposure of interest) would miss individuals who died in their driveways and thus never reached a hospital; this eventuality might greatly lower the odds ratio of infarction associated with this strenuous activity.

24. Detection (unmasking) • An exposure might lead to a search for an outcome, as well as the outcome itself Example: Estrogen replacement therapy might cause symptom less endometrial cancer patients to bleed, resulting in initiation of diagnostic tests. In this instance, the exposure unmasked the subclinical cancer, leading to a spurious increase in the odds ratio.

25. Non-response bias • Another source of selection bias derives from patients or control refusal or non response to inquiries requesting participation in a study. • A refusal may be prompted by a temporary inconvenience in a person’s schedule, by a particularly frustrating day with an unusual number of aggravations or by some subliminal distaste for the tone or demeanor of the interviewer making the initial contact

26. Information bias • Information bias, also known as observation, classification, or measurement bias, resulting from incorrect determination of exposure or outcome, or both • In a case-control study, information about exposure should be a gathered in the same way for cases and controls Example: An investigator might gather information about an exposure at bedside for a case but by telephone from a community control

27. Information bias Misclassification of exposure Example: Exposure= exposure to cold virus Those who develop cold are more likely to identify the exposure than those who do not Case- “yes, I was sneezed on” Control- “no, can’t remember any sneezing”

28. Types of information bias • Recall bias • Diagnostic suspicion bias

29. Recall bias • Cases tend to search their memories to identify what might have caused their disease; healthy controls have no such motivation Example: A women questioned about frequency of coitus and number of different sexual partners may search her memory with more care and intensity if she has cervical cancer that would a healthy woman Mothers of infants born with birth defects (cases) may be more likely than mothers of normal infants (controls) to recall or even overestimate their use of over the counter medication during pregnancy

30. Diagnostic suspicion bias • Implies that knowledge of a putative cause of disease might launce a more intensive search for the disease among those exposed. Example: Preferentially searching for infaction by HIV in intavenous drug users

31. Confounding • A third factor which is related to both exposure and outcome Example; Association between child’s birth rank (exposure) and Down syndrome (outcome) Confounding variable- mother’s age

32. Strengths • Can examine multiple etiologic factors for a single disease (e.g. cardiovascular disease) • Is optimal for the study of rare disease (e.g.leukaemia in adolescents) • Is particularly well suited to the evaluation of disease with long latent period (e.g. cancer) • Is relatively quick and inexpensive compared with other analytical designs

33. Limitations • Cannot directly compute incidence rates of disease in exposed and non-exposed individuals, unless study is population based • In some situations, the temporal relationship between exposure and disease may be difficult to establish • Is particularly prone to bias compared with other analytical designs, in particular selection and recall bias • Difficult to assemble a representative case group and control group