Validation and updating of predictive logistic regression models: a study on sample size and shrinkage
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 probabi...
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Published in | Statistics in medicine Vol. 23; no. 16; pp. 2567 - 2586 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
Chichester, UK
John Wiley & Sons, Ltd
30.08.2004
Wiley Subscription Services, Inc |
Subjects | |
Online Access | Get full text |
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Summary: | 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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Bibliography: | istex:F0665747081242B213781754BA689ED38769DE5E ark:/67375/WNG-JSF79ZMX-R ArticleID:SIM1844 ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23 |
ISSN: | 0277-6715 1097-0258 |
DOI: | 10.1002/sim.1844 |