In many real-world applications, such as those based on electronic health records, prognostic prediction of patient survival is based on heterogeneous sets of clinical laboratory measurements. To address the trade-off between the predictive accuracy of a prognostic model and the costs related to its clinical implementation, we propose an optimized L0-pseudonorm approach to learn sparse solutions in multivariable regression. The model sparsity is maintained by restricting the number of nonzero coefficients in the model with a cardinality constraint, which makes the optimization problem NP-hard. In addition, we generalize the cardinality constraint for grouped feature selection, which makes it possible to identify key sets of predictors that may be measured together in a kit in clinical practice. We demonstrate the operation of our cardinality constraint-based feature subset selection method, named OSCAR, in the context of prognostic prediction of prostate cancer patients, where it enables one to determine the key explanatory predictors at different levels of model sparsity. We further explore how the model sparsity affects the model accuracy and implementation cost. Lastly, we demonstrate generalization of the presented methodology to high-dimensional transcriptomics data.

Motivation: Prognostic models are widely used in clinical decision-making, such as risk stratification and tailoring treatment strategies, with the aim to improve patient outcomes while reducing overall healthcare costs. While prognostic models have been adopted into clinical use, benchmarking their performance has been difficult due to lack of open clinical datasets. The recent DREAM 9.5 Prostate Cancer Challenge carried out an extensive benchmarking of prognostic models for metastatic Castration-Resistant Prostate Cancer (mCRPC), based on multiple cohorts of open clinical trial data. Results: We make available an open-source implementation of the top-performing model, ePCR, along with an extended toolbox for its further re-use and development, and demonstrate how to best apply the implemented model to real-world data cohorts of advanced prostate cancer patients. Availability and implementation: The open-source R-package ePCR and its reference documentation are available at the Central R Archive Network (CRAN): https://CRAN.R-project.org/package=ePCR. R-vignette provides step-by-step examples for the ePCR usage. Supplementary information: Supplementary data are available at Bioinformatics online.