Flexible propensity score estimation strategies for clustered data in observational studies.

ABSTRACT: Existing studies have suggested superior performance of nonparametric machine learning over logistic regression for propensity score estimation. However, it is unclear whether the advantages of nonparametric propensity score modeling are carried to settings where there is clustering of individuals, especially when there is unmeasured cluster-level confounding. In this work we examined the performance of logistic regression (all main effects), Bayesian additive regression trees and generalized boosted modeling for propensity score weighting in clustered settings, with the clustering being accounted for by including either cluster indicators or random intercepts. We simulated data for three hypothetical observational studies of varying sample and cluster sizes. Confounders were generated at

SUBMITTER: Chang TH 

PROVIDER: S-EPMC9996644 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature