期刊
STATISTICS IN MEDICINE
卷 38, 期 22, 页码 4290-4309出版社
WILEY
DOI: 10.1002/sim.8296
关键词
heterogeneity; meta-analysis; prediction; regression modeling
类别
资金
- Patient-Centered Outcomes Research Institute [SA.Tufts.PARC.OSCO.2018.01.25, ME-1606-35555]
- FORECEE (4C) Project Horizon 2020 [634570]
- Movember Foundation [GAP3]
- Netherlands Organization for Health Research and Development [91617050, 91810615]
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.
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