Kernel machine testing for risk prediction with stratified case cohort studies.

ABSTRACT: Large assembled cohorts with banked biospecimens offer valuable opportunities to identify novel markers for risk prediction. When the outcome of interest is rare, an effective strategy to conserve limited biological resources while maintaining reasonable statistical power is the case cohort (CCH) sampling design, in which expensive markers are measured on a subset of cases and controls. However, the CCH design introduces significant analytical complexity due to outcome-dependent, finite-population sampling. Current methods for analyzing CCH studies focus primarily on the estimation of simple survival models with linear effects; testing and estimation procedures that can efficiently capture complex non-linear marker effects for CCH data remain elusive. In this article, we propose inverse pr

SUBMITTER: Payne R 

PROVIDER: S-EPMC4899160 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature