Robust risk prediction with biomarkers under two-phase stratified cohort design.

ABSTRACT: Identification of novel biomarkers for risk prediction is important for disease prevention and optimal treatment selection. However, studies aiming to discover which biomarkers are useful for risk prediction often require the use of stored biological samples from large assembled cohorts, and thus the depletion of a finite and precious resource. To make efficient use of such stored samples, two-phase sampling designs are often adopted as resource-efficient sampling strategies, especially when the outcome of interest is rare. Existing methods for analyzing data from two-phase studies focus primarily on single marker analysis or fitting the Cox regression model to combine information from multiple markers. However, the Cox model may not fit the data well. Under model misspecification, the com

SUBMITTER: Payne R 

PROVIDER: S-EPMC5045782 | biostudies-literature | 2016 Dec

REPOSITORIES: biostudies-literature