Tuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters.

TitleTuning Parameter Selection in Cox Proportional Hazards Model with a Diverging Number of Parameters.
Publication TypeJournal Article
Year of Publication2018
AuthorsNi, Ai, and Jianwen Cai
JournalScand Stat Theory Appl
Volume45
Issue3
Pagination557-570
Date Published2018 Sep
ISSN0303-6898
Abstract

Regularized variable selection is a powerful tool for identifying the true regression model from a large number of candidates by applying penalties to the objective functions. The penalty functions typically involve a tuning parameter that control the complexity of the selected model. The ability of the regularized variable selection methods to identify the true model critically depends on the correct choice of the tuning parameter. In this study we develop a consistent tuning parameter selection method for regularized Cox's proportional hazards model with a diverging number of parameters. The tuning parameter is selected by minimizing the generalized information criterion. We prove that, for any penalty that possesses the oracle property, the proposed tuning parameter selection method identifies the true model with probability approaching one as sample size increases. Its finite sample performance is evaluated by simulations. Its practical use is demonstrated in the Cancer Genome Atlas (TCGA) breast cancer data.

DOI10.1111/sjos.12313
Alternate JournalScand Stat Theory Appl
Original PublicationTuning parameter selection in Cox proportional hazards model with a diverging number of parameters.
PubMed ID30147217
PubMed Central IDPMC6107315
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States
P30 CA008748 / CA / NCI NIH HHS / United States
R01 ES021900 / ES / NIEHS NIH HHS / United States
Project: