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

arXiv: https://arxiv.org/abs/1307.6417 · HTML: https://mlsanity.com/paper/1307.6417/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jul 2013.

## Built on by (most cited first, 6 of 6)

- [DeepMTS: Deep Multi-task Learning for Survival Prediction in Patients with Advanced Nasopharyngeal Carcinoma using Pretreatment PET/CT](https://mlsanity.com/paper/2109.07711/) (2021)
- [Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures](https://mlsanity.com/paper/2112.04828/) (2021)
- [EOCSA: Predicting Prognosis of Epithelial Ovarian Cancer with Whole Slide Histopathological Images](https://mlsanity.com/paper/2210.05258/) (2022)
- [EPIC-Survival: End-to-end Part Inferred Clustering for Survival Analysis, Featuring Prognostic Stratification Boosting](https://mlsanity.com/paper/2101.11085/) (2021)
- [Multimodal Learning for Non-small Cell Lung Cancer Prognosis](https://mlsanity.com/paper/2211.03280/) (2022)
- [Boosting methods for interval-censored data with regression and classification](https://mlsanity.com/paper/2601.17973/) (2026)

## Abstract (arXiv)

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.