Boosting the concordance index for survival data - a unified framework to derive and evaluate biomarker combinations

TL;DR

This paper introduces a unified boosting framework based on the concordance index to derive and evaluate biomarker combinations for survival prediction, improving discriminatory power in molecular data.

Contribution

It proposes a novel component-wise boosting algorithm that optimizes biomarker combinations specifically for the concordance index, addressing methodological inconsistencies in previous approaches.

Findings

The new method outperforms traditional approaches in simulation studies.

It achieves higher discriminatory power in breast cancer survival data.

The framework provides a more consistent evaluation of biomarker combinations.

Abstract

The development of molecular signatures for the prediction of time-to-event outcomes is a methodologically challenging task in bioinformatics and biostatistics. Although there are numerous approaches for the derivation of marker combinations and their evaluation, the underlying methodology often suffers from the problem that different optimization criteria are mixed during the feature selection, estimation and evaluation steps. This might result in marker combinations that are only suboptimal regarding the evaluation criterion of interest. To address this issue, we propose a unified framework to derive and evaluate biomarker combinations. Our approach is based on the concordance index for time-to-event data, which is a non-parametric measure to quantify the discrimatory power of a prediction rule. Specifically, we propose a component-wise boosting algorithm that results in linear biomarker combinations that are optimal with respect to a smoothed version of the concordance index. We investigate the performance of our algorithm in a large-scale simulation study and in two molecular data sets for the prediction of survival in breast cancer patients. Our numerical results show that the new approach is not only methodologically sound but can also lead to a higher discriminatory power than traditional approaches for the derivation of gene signatures.