ArticleBioinformatics (Oxford, England)2022
Avoiding C-hacking when evaluating survival distribution predictions with discrimination measures.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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6 citing papers in PubMed.
- A large-scale neutral comparison study of survival models on low-dimensional data.Bioinformatics (Oxford, England) · 2026Article
- Prognostic biomarker discovery in pancreatic cancer through hybrid ensemble feature selection and multi-omics data.BioData mining · 2026Article
- Penalized landmark supermodels (penLM) for dynamic prediction for time-to-event outcomes in high-dimensional data.BMC medical research methodology · 2025Article
- Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study.BMC medical informatics and decision making · 2024Article
- Tutorial on survival modeling with applications to omics data.Bioinformatics (Oxford, England) · 2024Article
- Systematic comparison of multi-omics survival models reveals a widespread lack of noise resistance.Cell reports methods · 2023Article
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3 authors.
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Abstract
motivationIn this article, we consider how to evaluate survival distribution predictions with measures of discrimination. This is non-trivial as discrimination measures are the most commonly used in survival analysis and yet there is no clear method to derive a risk prediction from a distribution prediction. We survey methods proposed in literature and software and consider their respective advantages and disadvantages.
resultsWhilst distributions are frequently evaluated by discrimination measures, we find that the method for doing so is rarely described in the literature and often leads to unfair comparisons or 'C-hacking'. We demonstrate by example how simple it can be to manipulate results and use this to argue for better reporting guidelines and transparency in the literature. We recommend that machine learning survival analysis software implements clear transformations between distribution and risk predictions in order to allow more transparent and accessible model evaluation. AVAILABILITY AND IMPLEMENTATION: The code used in the final experiment is available at https://github.com/RaphaelS1/distribution_discrimination.
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