A framework for meta-analysis of prediction models for binary and time-to-event outcomes

Article type
Authors
Debray T1, Damen J1, Reitsma H1, Collins G2, Hooft L1, Riley R3, Moons K1
1Julius Center for Health Sciences and Primary Care
2Oxford University
3Keele University
Abstract
Background: It is widely recommended that any developed - diagnostic or prognostic - prediction model should be externally validated across different settings and populations. When multiple validations have been performed, a systematic review followed by a formal meta analysis may help to understand whether and under what circumstances the model performs accurately or requires further improvements.

Objectives: To discuss methods for summarising the performance of prediction models with both binary and time-to-event outcomes.

Methods: We present statistical methods for dealing with incomplete reporting (of performance and precision estimates), and to obtain time-specific summary estimates of the c-statistic, the calibration-in-the-large and the calibration slope. In addition, we provide guidance on the implementation of a Bayesian estimation framework, and discuss several empirically based prior distributions. All methods are illustrated in two example reviews where we evaluate the predictive performance of EuroSCORE II and Framingham Wilson.