ArticlePloS one2014
Boosting the concordance index for survival data--a unified framework to derive and evaluate biomarker combinations.
Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers.
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Who cites it
48 citing papers in PubMed.
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- Deep learning-driven survival prediction in pan-cancer studies by integrating multimodal histology-genomic data.Briefings in bioinformatics · 2025Article
- Development of an Endoplasmic Reticulum Stress-Related Diagnostic Signature in Polycystic Ovary Syndrome.Reproductive sciences (Thousand Oaks, Calif.) · 2025Article
- Establishing a Ten Disulfidptosis-related Gene Signature for Prognostic Prediction in Skin Cutaneous Melanoma.Combinatorial chemistry & high throughput screening · 2025Article
- Genetic Prediction Modeling in Large Cohort Studies via Boosting Targeted Loss Functions.Statistics in medicine · 2024Article
- Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma.Heliyon · 2024Article
- Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.Diagnostics (Basel, Switzerland) · 2024Article
- Predictive Role of a Novel Ferroptosis-Related lncRNA Pairs Model in the Prognosis of Papillary Thyroid Carcinoma.Biochemical genetics · 2024Article
- Nine-Gene Prognostic Signature Related to Gut Microflora for Predicting the Survival in Gastric Cancer Patients.The Turkish journal of gastroenterology : the official journal of Turkish Society of Gastroenterology · 2024Article
- A risk model based on lncRNA-miRNA-mRNA gene signature for predicting prognosis of patients with bladder cancer.Cancer biomarkers : section A of Disease markers · 2024Article
- Identification of Diagnostically Relevant Biomarkers in Patients with Coronary Artery Disease by Comprehensive Analysis.Journal of inflammation research · 2024Article
- Biology-aware mutation-based deep learning for outcome prediction of cancer immunotherapy with immune checkpoint inhibitors.NPJ precision oncology · 2023Article
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- A risk score model based on endoplasmic reticulum stress related genes for predicting prognostic value of osteosarcoma.BMC musculoskeletal disorders · 2023Article
- Improved Survival Analyses Based on Characterized Time-Dependent Covariates to Predict Individual Chronic Kidney Disease Progression.Biomedicines · 2023Article
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2 authors.
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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 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 discriminatory power of a prediction rule. Specifically, we propose a gradient 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.
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