A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
Debray TPA, Damen JAA, Riley RD, Snell K, Reitsma JB, Hooft L, Collins GS, Moons KGM
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".
Cite as: Debray TPA, Damen JAA, Riley RD, Snell K, Reitsma JB, Hooft L, Collins GS, Moons KGM. A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Stat Methods Med Res 2018, volume 0. DOI: 10.1177/0962280218785504.