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CME Disclosure Statement The North Shore LIJ Health System adheres to the ACCME's new Standards for Commercial Support. Any individuals in a position to control the content of a CME activity, including faculty, planners and managers, are required to disclose all financial relationships with commercial interests. All identified potential conflicts of interest are thoroughly vetted by the North Shore-LIJ for fair balance and scientific objectivity and to ensure appropriateness of patient care recommendations. Course Director, Kevin Tracey, has disclosed a commercial interest in Setpoint, Inc. as the cofounder, for stock and consulting support. He has resolved his conflicts by identifying a faculty member to conduct content review of this program who has no conflicts. The speaker, Martin L. Lesser, PhD, has no conflicts. 2

Case Control Studies

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10 Situations in Which Matching on F is Proper E E D D FF Situation A Situation B A: Indirect association between E and D E=alcohol use D=lung cancer F=smoking B:E and F are both risk factors and are associated with each other E=OC use D=myocardial infarction F=smoking Reference: Schlesselman, 1982 Copyright

11 E E D D FF Situations in Which Matching on F is Unnecessary Situation C Situation D C:F is associated with D independently of E, but F is not associated with E E=blood group O D=venous thromboembolism F=age D:F is associated with E, but is not a risk factor for D E=regular exercise D=(lack of) MI F=fluid intake Reference: Schlesselman, 1982 Copyright

12 Logistics of Matching 1. Specification of matching criteria (categorical, “calipers”, etc.) 2. Individual (1:1, 1:n) vs. frequency matching Copyright

13 Propensity Score Matching Copyright A method of matching subjects in treatment and control groups with similar propensity scores (i.e. the propensity to receive treatment based upon a set of certain covariates). Goal: Find subjects with the same a priori likelihood of being in the treatment group. Reduces bias in measured characteristics only

14 Propensity Score Matching Copyright Use logistic regression to model the probability of being in the treatment group. Include any covariates that may be related to receiving treatment. 2.Predict the probability of being in the treatment group (propensity score). 3.Match subjects with the “same” propensity score (one in the treatment group, one in the control group). ** Don’t have to match exactly Caliper’s matching 4.Use standard analyses for matched data 5.Calculate standardized differences after matching to check for balance. |Stzd D| > 10% indicates poor balance.

15 Sources of Bias 1.Recall bias (cases are more (or less) likely to recall exposure than controls) 2.Reporting accuracy bias (cases or more (or less) likely to report accurately) 3.Selection bias (cases are not representative of those with disease) 4.Detection bias (exposed subjects are more (or less) likely to be screened for disease) 5.Referral bias (controls are referred to the health care system for an “unrelated” health problem, that is actually related to the disease under study) Copyright

16 Computing the Odds Ratio Copyright

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Case-Control vs. Cohort Study Rare diseases Diseases with long latency Data quality Relatively inexpensive Estimate incidence rate of disease Little or no risk to subjects Requires comparatively fewer subjects Describe natural history of diseases Relatively quick to mount and conduct Allows study of multiple potential causes of a disease Allows study of multiple potential effects of an exposure Case Control Cohort (prospective) Copyright

Cohort Studies

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Cohort vs. Case-Control Study Rare diseases Diseases with long latency Data quality Relatively inexpensive Estimate incidence rate of disease Little or no risk to subjects Requires comparatively fewer subjects Describe natural history of diseases Relatively quick to mount and conduct Allows study of multiple potential causes of a disease Allows study of multiple potential effects of an exposure Cohort (prospective) Case-Control