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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.

Author information

1
Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, The Netherlands.
2
Department of Public Health, Erasmus MC, Rotterdam, The Netherlands.
3
Julius Center for Health Sciences and Primary Care, Utrecht University Medical Center, Utrecht, The Netherlands.
4
Cochrane Netherlands, University Medical Center Utrecht, Utrecht, The Netherlands.

Abstract

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.

KEYWORDS:

heterogeneity; meta-analysis; prediction; regression modeling

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