Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints.

ABSTRACT: Identifying informative predictors in a high dimensional regression model is a critical step for association analysis and predictive modeling. Signal detection in the high dimensional setting often fails due to the limited sample size. One approach to improving power is through meta-analyzing multiple studies which address the same scientific question. However, integrative analysis of high dimensional data from multiple studies is challenging in the presence of between-study heterogeneity. The challenge is even more pronounced with additional data sharing constraints under which only summary data can be shared across different sites. In this paper, we propose a novel data shielding integrative large-scale testing (DSILT) approach to signal detection allowing between-study heterogeneity and

SUBMITTER: Liu M 

PROVIDER: S-EPMC10327421 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature