By the same authors

Low-dimensional confounder adjustment and high-dimensional penalized estimation for survival analysis

Research output: Contribution to journalArticlepeer-review

Published copy (DOI)



Publication details

JournalLifetime Data Analysis
DateE-pub ahead of print - 13 Oct 2015
DatePublished (current) - 1 Oct 2016
Issue number4
Number of pages23
Pages (from-to)547-569
Early online date13/10/15
Original languageEnglish


High-throughput profiling is now common in biomedical research. In this paper we consider the layout of an etiology study composed of a failure time response, and gene expression measurements. In current practice, a widely adopted approach is to select genes according to a preliminary marginal screening and a follow-up penalized regression for model building. Confounders, including for example clinical risk factors and environmental exposures, usually exist and need to be properly accounted for. We propose covariate-adjusted screening and variable selection procedures under the accelerated failure time model. While penalizing the high-dimensional coefficients to achieve parsimonious model forms, our procedure also properly adjust the low-dimensional confounder effects to achieve more accurate estimation of regression coefficients. We establish the asymptotic properties of our proposed methods and carry out simulation studies to assess the finite sample performance. Our methods are illustrated with a real gene expression data analysis where proper adjustment of confounders produces more meaningful results.

    Research areas

  • Accelerated failure time model, Confounder adjustment, Gene expression, Independent screening, Variable selection

Discover related content

Find related publications, people, projects, datasets and more using interactive charts.

View graph of relations