Identification of homogeneous and heterogeneous variables in pooled cohort studies.

ABSTRACT: Pooled analyses integrate data from multiple studies and achieve a larger sample size for enhanced statistical power. When heterogeneity exists in variables' effects on the outcome across studies, the simple pooling strategy fails to present a fair and complete picture of the effects of heterogeneous variables. Thus, it is important to investigate the homogeneous and heterogeneous structure of variables in pooled studies. In this article, we consider the pooled cohort studies with time-to-event outcomes and propose a penalized Cox partial likelihood approach with adaptively weighted composite penalties on variables' homogeneous and heterogeneous effects. We show that our method can characterize the variables as having heterogeneous, homogeneous, or null effects, and estimate non-zero effec

SUBMITTER: Cheng X 

PROVIDER: S-EPMC4745128 | biostudies-literature | 2015 Jun

REPOSITORIES: biostudies-literature