Adverse subpopulation regression for multivariate outcomes with high-dimensional predictors.

ABSTRACT: Biomedical studies have a common interest in assessing relationships between multiple related health outcomes and high-dimensional predictors. For example, in reproductive epidemiology, one may collect pregnancy outcomes such as length of gestation and birth weight and predictors such as single nucleotide polymorphisms in multiple candidate genes and environmental exposures. In such settings, there is a need for simple yet flexible methods for selecting true predictors of adverse health responses from a high-dimensional set of candidate predictors. To address this problem, one may either consider linear regression models for the continuous outcomes or convert these outcomes into binary indicators of adverse responses using predefined cutoffs. The former strategy has the disadvantage of oft

SUBMITTER: Zhu B 

PROVIDER: S-EPMC3712761 | biostudies-literature | 2012 Dec

REPOSITORIES: biostudies-literature