High-dimensional propensity scores for empirical covariate selection in secondary database studies: Planning, implementation, and reporting.

ABSTRACT: Real-world evidence used for regulatory, payer, and clinical decision-making requires principled epidemiology in design and analysis, applying methods to minimize confounding given the lack of randomization. One technique to deal with potential confounding is propensity score (PS) analysis, which allows for the adjustment for measured preexposure covariates. Since its first publication in 2009, the high-dimensional propensity score (hdPS) method has emerged as an approach that extends traditional PS covariate selection to include large numbers of covariates that may reduce confounding bias in the analysis of healthcare databases. hdPS is an automated, data-driven analytic approach for covariate selection that empirically identifies preexposure variables and proxies to include in the PS mod

SUBMITTER: Rassen JA 

PROVIDER: S-EPMC10099872 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature