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Topics in Biostatistics: Propensity Score Analysis
June 23, 2016 @ 2:00 pm - 3:30 pm
Use of propensity scores to identify and control for confounding in observational studies that relate medications or surgical treatments to outcomes has increased substantially in recent years. However, it remains controversial whether, and if so when, use of propensity scores provides estimates of treatment effects that are less biased than those obtained from conventional multivariate models. In the great majority of published studies that have used both approaches, estimated effects from propensity score and regression methods have been similar. Simulation studies further suggest comparable performance of the two approaches in many settings.
We discuss five reasons that favor use of propensity scores:
– the value of focus on indications for treatment
– optimal matching strategies from alternative designs
– improved control for confounding with many potential confounders and scarce outcomes
– ability to identify interactions between propensity of treatment and treatment effects on outcomes
– and correction for unobserved confounders via propensity score calibration