Measuring the Performance of Survival Models to Personalize Treatment Choices

Various statistical and machine learning algorithms can be used to predict treatment effects at the patient level using data from randomized clinical trials (RCTs). Such predictions can facilitate individualized treatment decisions. Recently, a range of methods and metrics were developed for assessing the accuracy of such predictions. Here, we extend these methods, focusing on the case of survival (time-to-event) outcomes. We start by providing alternative definitions of the participant-level treatment benefit; subsequently, we summarize existing and propose new measures for assessing the performance of models estimating participant-level treatment benefits. We explore metrics assessing discrimination and calibration for benefit and decision accuracy. These measures can be used to assess the performance of statistical as well as machine learning models and can be useful during model development (i.e., for model selection or for internal validation) or when testing a model in new settings (i.e., in an external validation). We illustrate methods using simulated data and real data from the OPERAM trial, an RCT in multimorbid older people, which randomized participants to either standard care or a pharmacotherapy optimization intervention. We provide R codes for implementing all models and measures.

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Publication Details

Journal
Statistics in Medicine
Published
2025-03-30
DOI
https://doi.org/10.1002/sim.70050
Citations
4
Primary Topic
Advanced Causal Inference Techniques
Type
article
Field-Weighted Citation Impact
10.52

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article

Measuring the Performance of Survival Models to Personalize Treatment Choices

Thomas P. A. Debray, Jeroen Hoogland, Nicolas Rodondi, Valerie Aponte Ribero et al.
4 citations
Statistics in Medicine
Advanced Causal Inference Techniques
10.52
article

Measuring the Performance of Survival Models to Personalize Treatment Choices

Thomas P. A. Debray, Jeroen Hoogland, Nicolas Rodondi, Valerie Aponte Ribero, Wilma Knol, Ian R. White, Orestis Efthimiou, Séverine Henrard, Huiberdina L. Koek, Matthias Schwenkglenks, Matthias Egger
article en
4 citations

Abstract

Various statistical and machine learning algorithms can be used to predict treatment effects at the patient level using data from randomized clinical trials (RCTs). Such predictions can facilitate individualized treatment decisions. Recently, a range of methods and metrics were developed for assessing the accuracy of such predictions. Here, we extend these methods, focusing on the case of survival (time-to-event) outcomes. We start by providing alternative definitions of the participant-level treatment benefit; subsequently, we summarize existing and propose new measures for assessing the performance of models estimating participant-level treatment benefits. We explore metrics assessing discrimination and calibration for benefit and decision accuracy. These measures can be used to assess the performance of statistical as well as machine learning models and can be useful during model development (i.e., for model selection or for internal validation) or when testing a model in new settings (i.e., in an external validation). We illustrate methods using simulated data and real data from the OPERAM trial, an RCT in multimorbid older people, which randomized participants to either standard care or a pharmacotherapy optimization intervention. We provide R codes for implementing all models and measures.

Statistics in MedicineVol. 44(7)
University of Bern (CH), Centraal Bureau voor de Statistiek (NL), University of Cape Town (ZA), University of Basel (CH), Utrecht University (NL), University Medical Center Utrecht (NL), University of Bristol (GB), Institute of Social and Preventive Medicine (CH), Amsterdam University Medical Centers (NL), Institut Scientifique de Santé Publique (BE), University College London (GB)
National Science Foundation, European Commission, Singapore Eye Research Institute, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Horizon 2020 Framework Programme, Medical Research Council, Staatssekretariat für Bildung, Forschung und Innovation
Peace, Justice and strong institutions, Reduced inequalities
Openalex Percentile: Top 2%
Advanced Causal Inference Techniques
10.52
DOIOpen Access PDFOpenAlex
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