Case control study

1. Case control study Moderator : ChetnaMaliye Presenter Reshma Sougaijam

2. Frame work • Introduction • Design and steps of case control study • Comparison of case control and cohort study • Advantage and disadvantage of case control study • Confounding and bias

3. Introduction • Types of epidemiological studies:

4. Case control studies: A case control study is defined as an epidemiological approach in which the researcher starts by picking up cases who have already developed a particular disease or outcome of interest , and a comparison group (controls) of subjects who except for the fact that they have not developed the particular disease, are otherwise similar to the case.

5. Design and steps of case control study

6. Steps of conducting case control study • Step 1- Specify the total population and actual (study population) • Step 2- Specify the measure study variables and their scales of measurement • Outcome variable: • Exposure variable • Specify the scale of measurement of the exposure variable • Potential confounding factor (PCF):

7. Step 3- Calculate the sample size : Thesample size may be estimated by • n= (Zα+Zβ)2(p₁q₁+p₂q₂)/(p₂-p₁)2 • where,Zα is the normal deviate corresponding to the level of the significance to be used in the test, • Zβ is the normal deviate corresponding to the two tailed probability • p₁ and p₂ are the proportions of exposed subjects in cases and control respectively. • q₁ = 1-p₁ and • q₂= 1-p₂

8. Example • To study the association of oral contraceptive use with breast cancer. • Proportion of OC use among breast cancer patient is 40% and proportion of OC use in the population who do not have breast cancer is 20%. With 95% Confidence interval, and 80% power of the study, we have • Zα=1.96, Zβ=1.65 • p₁ =40 p₂=20 • q₁ = 60q₂ =80 • Hence n= (Zα+Zβ)2(p₁q₁+p₂q₂)/(p₂-p₁)2 =(1.96+1.65)²(40x60+20x80)/(20-40) =(3.23)² (4000)/(20)² =10.43x4000/400 =417.2/4 =104

9. Step 4- specify the selection criteria of cases • Diagnostic criteria • State the inclusion or exclusion criteria • Sources of cases: Cases may be drawn from • Hospitals b. General population • Incident or prevalent cases

10. Step 5- Specify the selection procedure of controls • Source of controls • Hospitals controls • General population • Relatives • Neighborhood controls • Exclusion / inclusion criteria • Number of control per case • Number of control group

11. Matching: Matching is defined as the process of selecting the controls so that they are similar to the cases in certain characteristics, such as age, race, sex, socioeconomic status, and occupation • Matching may be of 2 types • Group matching (frequency matching) • Individual matching

12. Problem with matching: • Practical problem with matching: if an attempt is made to match according to too many characteristics, it may prove difficult or impossible to identify an appropriate control 2. Conceptual problem with matching: once we matched control to cases according to a given characteristics, we cannot study that characteristic.

13. Step 6- specify the procedures of measurement and specially take care to ensure validity and reliability • The basic measurement should have two essential requirements. That is, it should be “valid” and “reliable” • The measurement process should be valid: the measurement which we are making and recording should correctly measure what we really intend to measure • Secondly the measurement process should have “Reliability”: this is the ability of a measurement process to give consistent results when repeated applications are made.

14. Step 7- Analysis of data • Calculate the odds ratio and its 95% confidence interval. • Control of confounding will require stratified analysis using Mantel-Haenszel technique or a multiple logistic regression.

15. Calculation of Odds Ratio in case control study • Odds Ratio in a case control study is defined as the ratio of the Odds that the cases were exposed to the odds that the control were exposed

16. Example of calculating odds ratios in case control studies: • Unmatched case control study in which controls were not matched to the cases. Example : Let us assume the following: a case control study is carried out in 10 case and 10 controls. N indicates non exposed individual and E indicate exposed individual.

17. Thus 6 of cases were exposed and 3 of control were exposed

18. The odds ratio in unmatched case control study equals the ratio of the cross product. Odds ratio=ad/bc=6x7/4x3=42/12=3.5

19. Calculating odds ratio in matched-pairs case-control study • The case control pairs that had the same exposure experience are termed concordant pairs, and those with different exposure experience are termed discordant pairs. Calculation of odds ratio in such a matched- pair study is based on the discordant pairs only The odds ratio for matched pairs is therefore the ratio of discordant pairs Odds ratio=b/c

20. Example: A case control study in which each case is matched with a control, resulting in 10 case-control pairs

21. Odds ratio=4/1=4

22. Difference between case control and cohort studies

23. Difference between case control and cohort studies

24. Advantage and disadvantages of different observational study designs

25. Advantages and disadvantages of case control studies • Advantages : • Inexpensive, requires only a few subjects gives quick results • Well suited for outcome which is rare • Helps in examining multiple etiologic factors- once we have the case of the disease, we can take history of all the factors that we feel may be risk factors • No attrition problem, because case control study do not require follow up of individuals into the future. • No risk to the subject

26. Advantages and disadvantages of case control studies • Disadvantages • Not a good method for studying rare exposure • Does not give ant idea of “incidence” or “prevalence”; it only gives us a measure of Odds Ratio • Prone to various forms of selection and information bias • Temporal relationship is not proven

27. Bias and confounding • Bias:. Bias has been defined as any systematic error in the design, conduct or analysis of a study that result in a mistaken estimate of an exposure’s effect on the risk of disease. • Types of bias: • Selection bias: selection bias is an error in selecting a study group or groups within the study and can have a major impact on the internal validity of the study and the legitimacy of the conclusion. 2. Information bias (measurement bias):): Information bias is a systematic error that arises because of incorrect information while making measurements on one or more variables in the study.

28. Types of selection bias: • Berksons’ bias (hospital selective admission): • Incidence-prevalence bias (Survivorship bias, Neyman’s bias

29. Types of information bias: • Recall bias: • Reporting bias • Observer’s (interviewer’s bias):

30. Confounding: • A confounding variable is defined as one which explains away the observed association between an exposure and an outcome variable.

31. Example : In a study to see the association of hypertension with coronary heart disease, age may be a confounding factor. • This is because, age is a known risk factor for CHD and age is associated with hypertension.

32. Control of confounding The methods commonly used to control confounding in the design of an epidemiological study are: • Randomization • Restriction • Matching

33. At the analysis stage, confounding can be controlled by • Stratification • Statistical modeling or multivariate analysis

34. References • R.Beaglehole, R.Bonita, T. Kjellstorm. Basic epidemiology. World Health Organization, Geneva: AITBS Publisher; 2006 • Bhalwar R et al. Text book of Public Health and Community Medicine 1st ed. Pune :Department of Community Medicine Armed Forces Medical College; 2009 • Leon G. Epidemiology 3rd ed. Philadelphia: Elsevier Saunders; 2004 • Mac Mahon B, Trichopoulos D. Epidemiology Principles and Methods 2nd ed. New York: Little, Brown and Company;1996 • Park K. Text Book of Preventive And Social Medicine 21st ed. India: M/s Banarsidas Bhanot;2011