Title: Comparing marginal estimators of propensity-adjusted treatment effects in studies with few observed outcome events

Abstract:  Nonrandomized studies of treatments from electronic healthcare databases are critical for producing the evidence necessary to making informed treatment decisions, but often rely on comparing rates of events observed in a small number of patients. In addition, a typical study constructed from an electronic healthcare database, for example, administrative claims data, requires adjustment for many, possibly hundreds, of potential confounders. Despite the importance of maximizing efficiency when there are many confounders and few observed outcome events, there has been relatively little research on the performance of different propensity score methods in this context. In this talk, I will describe and compare a wide variety of propensity-adjusted estimators of the marginal relative risk. In contrast to prior research that has focused on specific statistical methods in isolation of other analytic choices, I instead consider a method to be defined by the complete multi-step process from propensity score modeling to final treatment effect estimation. I evaluate methods via a “plasmode” simulation study, which creates simulated data sets based on a real cohort study of 2 treatments constructed from administrative claims data. Our results suggest a reconsideration of the most popular approaches to propensity score adjustment in this context.