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An important task in clinical medicine is the construction of risk predictionmodels for specific subgroups of patients based on high-dimensional molecularmeasurements such as gene expression data. Major objectives in modelinghigh-dimensional data are good prediction performance and feature selection tofind a subset of predictors that are truly associated with a clinical outcomesuch as a time-to-event endpoint. In clinical practice, this task ischallenging since patient cohorts are typically small and can be heterogeneouswith regard to their relationship between predictors and outcome. When data ofseveral subgroups of patients with the same or similar disease are available,it is tempting to combine them to increase sample size, such as in multicenterstudies. However, heterogeneity between subgroups can lead to biased resultsand subgroup-specific effects may remain undetected. For this situation, wepropose a penalized Cox regression model with a weighted version of the Coxpartial likelihood that includes patients of all subgroups but assigns themindividual weights based on their subgroup affiliation. Patients who are likelyto belong to the subgroup of interest obtain higher weights in thesubgroup-specific model. Our proposed approach is evaluated through simulationsand application to real lung cancer cohorts. Simulation results demonstratethat our model can achieve improved prediction and variable selection accuracyover standard approaches.

Weighted Cox regression for the prediction of heterogeneous patient subgroups