Debray, T, Damen, J, Riley, RD ORCID:, Snell, K, Reitsma, J, Hooft, L, Collins, G and Moons, K (2018) A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Statistical Methods in Medical Research.

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It is widely recommended that any developed—diagnostic or prognostic—prediction model is externally validated in terms of its predictive performance measured by calibration and discrimination. When multiple validations have been performed, a systematic review followed by a formal meta-analysis helps to summarize overall performance across multiple settings, and reveals under which circumstances the model performs suboptimal (alternative poorer) and may need adjustment. We discuss how to undertake meta-analysis of the performance of prediction models with either a binary or a time-to-event outcome. We address how to deal with incomplete availability of study-specific results (performance estimates and their precision), and how to produce summary estimates of the c-statistic, the observed:expected ratio and the calibration slope. Furthermore, we discuss the implementation of frequentist and Bayesian meta-analysis methods, and propose novel empirically-based prior distributions to improve estimation of between-study heterogeneity in small samples. Finally, we illustrate all methods using two examples: meta-analysis of the predictive performance of EuroSCORE II and of the Framingham Risk Score. All examples and meta-analysis models have been implemented in our newly developed R package “metamisc”.

Item Type: Article
Uncontrolled Keywords: meta-analysis, aggregate data, evidence synthesis, systematic review, prognosis, validation, prediction, discrimination, callibration
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > Primary Care Health Sciences
Depositing User: Symplectic
Date Deposited: 02 May 2018 07:38
Last Modified: 08 Mar 2019 14:14

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