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Stat Med. 2019 Sep 30;38(22):4290-4309. doi: 10.1002/sim.8296. Epub 2019 Aug 2.

Assessment of heterogeneity in an individual participant data meta-analysis of prediction models: An overview and illustration.

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Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.
Julius Center for Health Sciences and Primary Care, Utrecht University Medical Center, Utrecht, The Netherlands.
Cochrane Netherlands, University Medical Center Utrecht, Utrecht, The Netherlands.


Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta-analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6-month mortality based on individual patient data using meta-analytic techniques (15 studies, n = 11 022 patients). The insights into various aspects of heterogeneity are important to develop better models and understand problems with the transportability of absolute risk predictions.


heterogeneity; meta-analysis; prediction; regression modeling

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