A Modified Adaptive Lasso for Identifying Interactions in the Cox Model with the Heredity Constraint.

ABSTRACT: In many biomedical studies, identifying effects of covariate interactions on survival is a major goal. Important examples are treatment-subgroup interactions in clinical trials, and gene-gene or gene-environment interactions in genomic studies. A common problem when implementing a variable selection algorithm in such settings is the requirement that the model must satisfy the strong heredity constraint, wherein an interaction may be included in the model only if the interaction's component variables are included as main effects. We propose a modified Lasso method for the Cox regression model that adaptively selects important single covariates and pairwise interactions while enforcing the strong heredity constraint. The proposed method is based on a modified log partial likelihood including

SUBMITTER: Wang L 

PROVIDER: S-EPMC4111275 | biostudies-literature | 2014 Oct

REPOSITORIES: biostudies-literature