Academic Journal
Measuring the Performance of Survival Models to Personalize Treatment Choices
| Τίτλος: | Measuring the Performance of Survival Models to Personalize Treatment Choices |
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| Συγγραφείς: | Orestis Efthimiou, Jeroen Hoogland, Thomas P. A. Debray, Valerie Aponte Ribero, Wilma Knol, Huiberdina L. Koek, Matthias Schwenkglenks, Séverine Henrard, Matthias Egger, Nicolas Rodondi, Ian R. White |
| Συνεισφορές: | MS Geriatrie, Circulatory Health, UCL - SSS/LDRI - Louvain Drug Research Institute, UCL - SSS/IRSS - Institut de recherche santé et société |
| Πηγή: | Stat Med Statistics in Medicine, Vol. 44, no.7, p. e70050 (2025) |
| Στοιχεία εκδότη: | Wiley, 2025. |
| Έτος έκδοσης: | 2025 |
| Θεματικοί όροι: | Models, Statistical, Randomized Controlled Trials as Topic/statistics & numerical data, Precision Medicine/methods, Survival Analysis, Machine Learning, Treatment Outcome, Journal Article, Humans, Computer Simulation, Precision Medicine, Algorithms, Research Article, Randomized Controlled Trials as Topic, Aged |
| Περιγραφή: | 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. |
| Τύπος εγγράφου: | Article Other literature type |
| Περιγραφή αρχείου: | application/pdf |
| Γλώσσα: | English |
| ISSN: | 1097-0258 0277-6715 |
| DOI: | 10.1002/sim.70050 |
| DOI: | 10.48620/87576 |
| Σύνδεσμος πρόσβασης: | https://pubmed.ncbi.nlm.nih.gov/40207416 https://dspace.library.uu.nl/handle/1874/461108 https://pure.amsterdamumc.nl/en/publications/83224a93-c553-47a0-a855-a63d5aa665ae https://doi.org/10.1002/sim.70050 https://hdl.handle.net/2078.1/302377 |
| Rights: | CC BY |
| Αριθμός Καταχώρησης: | edsair.doi.dedup.....f27c1f33d41bef1f8fb718f5594544d8 |
| Βάση Δεδομένων: | OpenAIRE |
| ISSN: | 10970258 02776715 |
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| DOI: | 10.1002/sim.70050 |