This Article Full Text (OnlineFirst PDF) Alert me when this article is cited Alert me if a correction is posted Services Email this article to a friend Similar articles in this journal Similar articles in PubMed Alert me to new issues of the journal Add to Saved Citations Download to citation manager Request Permissions Request Reprints Add to My Marked Citations Citing Articles Citing Articles via Scopus Google Scholar Articles by Witten, D. M Articles by Tibshirani, R. PubMed PubMed Citation Articles by Witten, D. M Articles by Tibshirani, R. Social Bookmarking                         What's this?

Survival analysis with high-dimensional covariates

¹ Department of Statistics, Stanford University, Stanford CA 94305, USA
² Departments of Health Research and Policy & Statistics, Stanford University, Stanford CA 94305, USA

^* To whom correspondence should be addressed. E-mail: dwitten{at}stanford.edu.

	Abstract

In recent years, breakthroughs in biomedical technology have led to a wealth of data in which the number of features (for instance, genes on which expression measurements are available) exceeds the number of observations (e.g. patients). Sometimes survival outcomes are also available for those same observations. In this case, one might be interested in (a) identifying features that are associated with survival (in a univariate sense), and (b) developing a multivariate model for the relationship between the features and survival that can be used to predict survival in a new observation. Due to the high dimensionality of this data, most classical statistical methods for survival analysis cannot be applied directly. Here, we review a number of methods from the literature that address these two problems.

First published on August 4, 2009
Statistical Methods in Medical Research 2009, doi:10.1177/0962280209105024


CiteULike    Complore    Connotea    Del.icio.us    Digg    Reddit    Technorati    Twitter    What's this?