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 language | English |
|---|---|
| Pages (from-to) | 4290-4309 |
| Number of pages | 20 |
| Journal | Statistics in Medicine |
| Volume | 38 |
| Issue number | 22 |
| DOIs | |
| Publication status | Published - 30 Sept 2019 |
Keywords
- heterogeneity
- meta-analysis
- prediction
- regression modeling
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