Covariate selection in high-dimensional propensity score analyses of treatment effects in small samples.

ABSTRACT: To reduce bias by residual confounding in nonrandomized database studies, the high-dimensional propensity score (hd-PS) algorithm selects and adjusts for previously unmeasured confounders. The authors evaluated whether hd-PS maintains its capabilities in small cohorts that have few exposed patients or few outcome events. In 4 North American pharmacoepidemiologic cohort studies between 1995 and 2005, the authors repeatedly sampled the data to yield increasingly smaller cohorts. They identified potential confounders in each sample and estimated both an hd-PS that included 0-500 covariates and treatment effects adjusted by decile of hd-PS. For sensitivity analyses, they altered the variable selection process to use zero-cell correction and, separately, to use only the variables' exposure asso

SUBMITTER: Rassen JA 

PROVIDER: S-EPMC3145392 | biostudies-literature | 2011 Jun

REPOSITORIES: biostudies-literature