Online Program Home
My Program

Sessions Were Renumbered as of May 19.

CC-W = McCormick Place Convention Center, West Building,   CC-N = McCormick Place Convention Center, North Building
H = Hilton Chicago,   UC= Conference Chicago at University Center
* = applied session       ! = JSM meeting theme

Activity Details

CE_30C Tue, 8/2/2016, 1:00 PM - 5:00 PM CC-W475b
Designing Observational Comparative Studies Using Propensity Score Methodology in Regulatory Settings (ADDED FEE) — Professional Development Continuing Education Course
ASA , Section on Medical Devices and Diagnostics
This course will introduce the causal inference framework and propensity score methods (e.g., matching, stratification, and weighting) and highlight the principle and importance of prospective design of observational comparative studies to increase the integrity and interpretability of outcome analysis results. Practical issues encountered in the application of the methodology in the regulatory settings will be presented, including study design process in regulatory submissions of drug, biologics, and medical devices for both pre-market and post-market studies; specification of treatment effects of interest in treatment comparisons (average treatment effect (ATE) or average treatment effect on the treated (ATT)); covariate identification and inclusion; control group selection/formation (a concurrent control, historical control, or control group extracted from national/international registry); sample size; and power consideration. Some differences for implementing propensity score methodology will be delineated for studies with different purposes, for regulatory submissions or general comparative effectiveness research. For example, exclusion of treated patients with an investigational product should be discouraged in studies aimed at pre-market regulatory submissions. These topics will be illustrated with examples based on regulatory review experience. Prerequisite: familiarity with general statistical methods in clinical study design and analysis.
Instructor(s): Donald B. Rubin, Harvard, Lilly Yue, FDA/CDRH/OSB
Copyright © American Statistical Association