title: 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 two adaptively weighted penalties, one for main effects and one for interactions. A two-dimensional tuning parameter for the penalties is determined by generalized cross-validation. Asymptotic properties are established, including consistency and rate of convergence, and it is shown that the proposed selection procedure has oracle properties, given proper choice of regularization parameters. Simulations illustrate that the proposed method performs reliably across a range of different scenarios.
authors: Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
authors: Lu
authors: L
authors: Wang
authors: Department of Biostatistics, University of Michigan, 1415 Washington Heights, Ann Arbor, MI 48109, USA.
authors: Jincheng
authors: J
authors: Shen
authors: Department of Biostatistics, M.D. Anderson Cancer Center, Houston, Texas 77030, USA.
authors: Peter F
authors: PF
authors: Thall
keywords: Modified adaptive Lasso; Oracle property; Penalized partial likelihood; Regularization; Variable selection