Validation and updating of predictive logistic regression models: a study on sample size and shrinkage
Abstract
A logistic regression model may be used to provide predictions of outcome for individual patients at another centre than where the model was developed. When empirical data are available from this centre, the validity of predictions can be assessed by comparing observed outcomes and predicted probabilities. Subsequently, the model may be updated to improve predictions for future patients. As an example, we analysed 30‐day mortality after acute myocardial infarction in a large data set (GUSTO‐I,n= 40 830). We validated and updated a previously published model from another study (TIMI‐II,n= 3339) in validation samples ranging from small (200 patients, 14 deaths) to large (10 000 patients, 700 deaths). Updated models were tested on independent patients. Updating methods included re‐calibration (re‐estimation of the intercept or slope of the linear predictor) and more structural model revisions (re‐estimation of some or all regression coefficients, model extension with more predictors). We applied heuristic shrinkage approaches in the model revision methods, such that regression coefficients were shrunken towards their re‐calibrated values. Parsimonious updating methods were found preferable to more extensive model revisions, which should only be attempted with relatively large validation samples in combination with shrinkage. Copyright © 2004 John Wiley & Sons, Ltd.
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Validation and updating of prediction models allows to account for information already captured in previous studies and make better use of existing evidence.
“In contrast to redevelopment, validation and updating of prediction models allows to (more effectively) account for information already captured in previous studies, and thus to make better use of existing evidence and data at hand.”
From A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes (2019) · cites this paper as [8]
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- Authors
- Steyerberg, E. W., Borsboom, G. J. J. M., van Houwelingen, H. C., Eijkemans, M. J. C., & Habbema, J. D. F.
- Affiliations
- Erasmus MC (NL)
- Corresponding author
- Steyerberg, Ewout W.
- Venue
- Statistics in Medicine
- Publisher
- Wiley
- Published
- 2004-07-27
- Volume
- 23
- Issue
- 16
- Pages
- 2567-2586
- ISSN
- 0277-6715 (ISSN-L)
- 1097-0258
- OpenAlex
- W2165139489
- Cited by
- 552
- References
- 37
- Citations by year
- 2026: 28 · 2025: 20 · 2024: 27 · 2023: 26 · 2022: 41 · 2021: 42 · 2020: 31 · 2019: 33 · 2018: 27 · 2017: 31 · 2016: 27 · 2015: 31 · 2014: 31 · 2013: 28 · 2012: 28
- FWCI
- 11.48
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Statistics in Medicine (publishedVersion)
- PubMed (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (publishedVersion)
- EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
- Funders
- Koninklijke Nederlandse Akademie van Wetenschappen
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- model validation, sample size, shrinkage, recalibration, 30-day mortality, acute myocardial infarction, model revision, external validation
- MeSH terms
- Computer Simulation, Data Interpretation, Statistical, Humans, Myocardial Infarction, Reproducibility of Results, Multicenter Studies as Topic, Logistic Models, Sample Size
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