Evaluating large-scale propensity score performance through real-world and synthetic data experiments.

ABSTRACT:

Background

Propensity score adjustment is a popular approach for confounding control in observational studies. Reliable frameworks are needed to determine relative propensity score performance in large-scale studies, and to establish optimal propensity score model selection methods.

Methods

We detail a propensity score evaluation framework that includes synthetic and real-world data experiments. Our synthetic experimental design extends the 'plasmode' framework and simulates survival data under known effect sizes, and our real-world experiments use a set of negative control outcomes with presumed null effect sizes. In reproductions of two published cohort studies, we compare two propensity score estimation methods that contrast in their model selection approach: L1-regulariz

SUBMITTER: Tian Y 

PROVIDER: S-EPMC6280944 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature