Survival prediction based on compound covariate under Cox proportional hazard models.

ABSTRACT: Survival prediction from a large number of covariates is a current focus of statistical and medical research. In this paper, we study a methodology known as the compound covariate prediction performed under univariate Cox proportional hazard models. We demonstrate via simulations and real data analysis that the compound covariate method generally competes well with ridge regression and Lasso methods, both already well-studied methods for predicting survival outcomes with a large number of covariates. Furthermore, we develop a refinement of the compound covariate method by incorporating likelihood information from multivariate Cox models. The new proposal is an adaptive method that borrows information contained in both the univariate and multivariate Cox regression estimators. We show that

SUBMITTER: Emura T 

PROVIDER: S-EPMC3480451 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature