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Background: The development of classification methods for personalizedmedicine is highly dependent on the identification of predictive geneticmarkers. In survival analysis it is often necessary to discriminate betweeninfluential and non-influential markers. Usually, the first step is to performa univariate screening step that ranks the markers according to theirassociations with the outcome. It is common to perform screening using Coxscores, which quantify the associations between survival and each of themarkers individually. Since Cox scores do not account for dependencies betweenthe markers, their use is suboptimal in the presence highly correlated markers.Methods: As an alternative to the Cox score, we propose thecorrelation-adjusted regression survival (CARS) score for right-censoredsurvival outcomes. By removing the correlations between the markers, the CARSscore quantifies the associations between the outcome and the set of"de-correlated" marker values. Estimation of the scores is based on inverseprobability weighting, which is applied to log-transformed event times. Forhigh-dimensional data, estimation is based on shrinkage techniques. Results:The consistency of the CARS score is proven under mild regularity conditions.In simulations, survival models based on CARS score rankings achieved higherareas under the precision-recall curve than competing methods. Two exampleapplications on prostate and breast cancer confirmed these results. CARS scoresare implemented in the R package carSurv. Conclusions: In research applicationsinvolving high-dimensional genetic data, the use of CARS scores for markerselection is a favorable alternative to Cox scores even when correlationsbetween covariates are low. Having a straightforward interpretation and lowcomputational requirements, CARS scores are an easy-to-use screening tool inpersonalized medicine research.

Correlation-Adjusted Regression Survival Scores for High-Dimensional Variable Selection