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

Statistics in Medicine
Thomas WelchowskiMatthias Schmid

Abstract

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. 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 the associations between the outcome and the set of "decorrelated" marker values. Estimation of the scores is based on inverse probability weighting, which is applied to log-transformed event times. For high-dimensional data, estimation is based on shrinkage techniques. The consistency of the CARS score is proven under mild regularity conditions. In simulations with high correlations, survival models based on CARS score rankings achieved higher areas under the precision-recall...Continue Reading

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Datasets Mentioned

BETA
GPL5474
GSE16560
GSE25066

Methods Mentioned

BETA
GTPase

Software Mentioned

PRROC
Bioconductor
survival
R
carSurv
R packages survival and glmnet
topGO
survAUC
R package glmnet
mvnfast

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