Bayesian variable selection for high dimensional predictors and self-reported outcomes.

ABSTRACT:

Background

The onset of silent diseases such as type 2 diabetes is often registered through self-report in large prospective cohorts. Self-reported outcomes are cost-effective; however, they are subject to error. Diagnosis of silent events may also occur through the use of imperfect laboratory-based diagnostic tests. In this paper, we describe an approach for variable selection in high dimensional datasets for settings in which the outcome is observed with error.

Methods

We adapt the spike and slab Bayesian Variable Selection approach in the context of error-prone, self-reported outcomes. The performance of the proposed approach is studied through simulation studies. An illustrative application is included using data from the Women's Health Initiative SNP Health Association

SUBMITTER: Gu X 

PROVIDER: S-EPMC7487595 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature