1. Case Control Study Dr Pravin Pisudde Moderator: Abhishek Raut

2. Framework Introduction Definition Steps – Specify the total population and actual(study) population – Specify the major study variables and their scales of measurement – Calculate sample size – Selection of Cases – Selection of Controls – Ensure validity and reliability – Pilot study – Conduct study – Analysis of Data and interpretation Matching Confounding Biases

3. Introduction Type of studyAlternative nameUnit of study Observational studies Descriptive studies Analytical studies  EcologicalCorrelationalpopulation  Cross-sectionalPrevalenceIndividuals  Case-controlCase referenceIndividuals  CohortFollow upIndividuals Experimental Randomized controlled trialsClinical trialsPatients Field trialsHealthy people Community trailsCommunity intervention studiesCommunities Types of epidemiological study

4. Historical background Evolution states back In 1830s Some additions were made in 19 th century Proper descriptive methodology came in first half of 20 th century with Broders comparing the 537 patients of lip Ca with 500 controls

5. Definition An analytical epidemiological approach in which study population consists of groups who either have (cases) or do not have a particular health problem or outcome (controls) of interest Also called as – Case-Referent – Case-Compeer – Trohoc – Retrospective – Case-Control

6. Design of Case Control Study

7. Steps 1.Specify the total population and actual(study) population Idea from which the cases have come better for selecting conntrol 2.Specify the major study variables and their scales of measurement Outcome variable Exposure variable Potential Confounding factor

8. 3. Sample size calculation Step will be skiped or will be explained on spot

9. 4. Selection of Cases Definition of cases – Diagnostic criteria Clear cut Literature review, expert opinion – Eligibility criteria Inclusion and exclusion criteria Sources of cases – Hospital – General population – Incident or prevalent cases Method of sampling – Systematic random sampling – Simple random sampling

10. 5. Selection of Controls Sources – Hospitals – Relatives/neighbourhood – General population Definition – Same as cases No. of controls per case – One case for one control – May be raised to 4 or 5 control per case Matching – To ensure comparability – Generally matched variables are age, sex, SES etc Confounders – Distributed unequally among the cases and controls

11. 6. Procedures of measurement and specially take care to ensure validity and reliability Selection & information bias Other biases: Berksonian bias, Selection of inappropriate cases & controls, Self selection bias, Surviourship bias, incidence & prevalence bias, Selection of wrong control group. 7. Pilot study Pre-testing on 5-10 cases and controls Redefine methodology 8. Conduct study

12. Analysis and interpretation of study CasesControlstotal exposurePresentaba+b absentcdc+d totala+cb+da+b+c+d =a/a+b ÷ c/c+d Exposure rates among cases and controls to suspected factor exposure Cases=a/a+b Control=b/b+d Estimation of risk Odds ratio

13. Matching Matching refers to selection of subjects in a manner that forces the distribution of certain factors to be similar in cases and controls Purpose: To adjust - effects of relevant co-factors. Matching in Design: Accounted in Analysis Misconception: – The goal is to make the case and control groups similar in all respects, except for disease status. An Optimal Matching Scheme involves only those variables which improve statistical efficiency or eliminate bias from the effect of interest.

14. Matching contd…. Which variables are appropriate for matching? – Risk factors from prior work may be identified for matching – Matching by interviewer or hospital may be used to balance out the effects of interviewer and observer errors – It is best to limit matching to basic descriptors (age, race, sex, etc) – Non-modifiable risk factors – Use few matching factors

15. Overzealous matching may have adverse effects: – Matching on a strong correlate of the exposure, which is not an independent risk factor for the outcome (overmatching) may lead to an underestimate of OR. – Matching may lead to a false sense of security that a particular variable is adequately controlled. Example – Outcome: Clear cell adenocarcinoma of the vagina in young women – Exposure: Prenatal exposure Diethylstilboestrol(DES) – Matching: History of threatened spontaneous abortion in mothers – Matching factor Indication of DES (strong correlate of exposure) MF is not known to be independent risk factor for outcome – Matching: Over-represents exposure in the control group and underestimates risk estimate

16. Confounding in case-control study and other epidemiological investigations, it is mandatory to control for the possible effect of extraneous variables, which might influence the outcome of the analyses when associated with both the exposure levels and the risk of disease. Such a phenomenon is known as “confounding” and the related variables are called “confounders”. For example, both in animals and in human beings, ageing is associated with the risk of numerous diseases (e.g., incidence of most cancers). If the exposure is not homogeneously distributed among different age groups, its effect may be hidden by the effect of ageing. Differently from the other biases (i.e., selection and information biases), confounding may be controlled both during the study design and at the stage of data analysis.

17. Biases in case control study Selection bias – statistical bias in which there is an error in choosing the individuals or groups to take part in a scientific study. Berksonian bias – studies which are based entirely on hospital studies. Information/Misclassification biases – Information from study population is systemtically inaccurate regarding disease or exposure under study. – The bias that arises in a clinical study because of misclassification of the level of exposure to the agent or factor being assessed and/or misclassification of the disease itself; a type of bias that occurs when measurement of information

18. Confounding Confounding involves error in the interpretation of what may be an accurate measurement. Confounding in epidemiology is mixing of the effect of the exposure under study on the disease (outcome) with that of a third factor that is associated with the exposure and an independent risk factor for the disease (even among individuals nonexposed to the exposure factor under study). Confounding can cause overestimation or underestimation of the true association and may even change the direction of the observed effect. Confounding is the initial association between alcohol consumption and lung cancer (confounded by smoking, which is associated with alcohol use and an independent risk factor for lung cancer). Likewise, an association between gambling and cancer is confounded by at least smoking and alcohol.

19. Confounders can be positive or negative. – Positive confounders cause overestimation of an association (which may be an inverse association), and negative confounders cause underestimation of an association. It is not easy to recognize confounders. A practical way to recognize confounder is to analyze the data with and without controlling for the potential confounders. If the estimate of the association differs remarkably when controlled for the variable, it is a confounder and should be controlled for (by stratification or multivariable analysis). To be able to do this, investigators should make every effort to obtain data on all available risk factors for the disease under study.