Correlation-adjusted regression survival scores for high-dimensional variable selection.

ABSTRACT:

Background

The development of classification methods for personalized medicine is highly dependent on the identification of predictive genetic markers. In survival analysis, it is often necessary to discriminate between influential and noninfluential markers. It is common to perform univariate screening using Cox scores, which quantify the associations between survival and each of the markers to provide a ranking. Since Cox scores do not account for dependencies between the markers, their use is suboptimal in the presence of highly correlated markers.

Methods

As an alternative to the Cox score, we propose the correlation-adjusted regression survival (CARS) score for right-censored survival outcomes. By removing the correlations between the markers, the CARS score quantifies

SUBMITTER: Welchowski T 

PROVIDER: S-EPMC6519238 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature