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In biomedical studies it is of substantial interest to develop riskprediction scores using high-dimensional data such as gene expression data forclinical endpoints that are subject to censoring. In the presence ofwell-established clinical risk factors, investigators often prefer a procedurethat also adjusts for these clinical variables. While accelerated failure time(AFT) models are a useful tool for the analysis of censored outcome data, itassumes that covariate effects on the logarithm of time-to-event are linear,which is often unrealistic in practice. We propose to build risk predictionscores through regularized rank estimation in partly linear AFT models, wherehigh-dimensional data such as gene expression data are modeled linearly andimportant clinical variables are modeled nonlinearly using penalized regressionsplines. We show through simulation studies that our model has better operatingcharacteristics compared to several existing models. In particular, we showthat there is a nonnegligible effect on prediction as well as feature selectionwhen nonlinear clinical effects are misspecified as linear. This work ismotivated by a recent prostate cancer study, where investigators collected geneexpression data along with established prognostic clinical variables and theprimary endpoint is time to prostate cancer recurrence.

Risk prediction for prostate cancer recurrence through regularized estimation with simultaneous adjustment for nonlinear clinical effects