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

  • Ewout W Steyerberg
  • , Daan Nieboer
  • , Thomas P A Debray
  • , Hans C van Houwelingen

Research output: Contribution to journal › Article › Academic › peer-review

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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.

Original languageEnglish
Pages (from-to)4290-4309
Number of pages20
JournalStatistics in Medicine
Volume38
Issue number22
DOIs
Publication statusPublished - 30 Sept 2019

Keywords

  • heterogeneity
  • meta-analysis
  • prediction
  • regression modeling

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