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Activity Details


CE_21C Tue, 8/6/2013, 8:30 AM - 5:00 PM W-Fortifications
Causal Inference and Its Application in Health Sciences — Continuing Education Course
ASA , Section on Statistics in Epidemiology
Instructor(s): Miguel A. Hernan, Harvard School of Public Health, Dylan S Small, University of Pennsylvania
Causal inference for clinical trials and observational studies has become crucial for better understanding effects and mechanisms of a treatment/program. Commonly used statistical methods provide measures of association that may lack a causal interpretation even when the investigator 'adjusts for' all potential confounders in the analysis. To eliminate the discordance between the causal goals and the associational methods, it is necessary to formally define causal concepts, identify the conditions required to estimate causal effects, and use analytical methods that, under those conditions, provide estimates that can be endowed with a causal interpretation. The first half of this short course presents a framework for causal inference from observational studies and recent methodological developments, with a special emphasis on complex longitudinal data. The second half of this short course focuses on instrumental variable methods for causal inference in clinical trials and observational studies. Instrumental variable methods control for unmeasured confounding, which is a central concern in many observational studies as well as in per protocol analyses of clinical trials.



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