Bayesian Estimation of Propensity Scores for Integrating Multiple Cohorts with High-Dimensional Covariates.

ABSTRACT: Comparative meta-analyses of groups of subjects by integrating multiple observational studies rely on estimated propensity scores (PSs) to mitigate covariate imbalances. However, PS estimation grapples with the theoretical and practical challenges posed by high-dimensional covariates. Motivated by an integrative analysis of breast cancer patients across seven medical centers, this paper tackles the challenges of integrating multiple observational datasets. The proposed inferential technique, called Bayesian Motif Submatrices for Covariates (B-MSC), addresses the curse of dimensionality by a hybrid of Bayesian and frequentist approaches. B-MSC uses nonparametric Bayesian "Chinese restaurant" processes to eliminate redundancy in the high-dimensional covariates and discover latent motifs</

SUBMITTER: Guha S 

PROVIDER: S-EPMC12320959 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature