Bayesian meta-analysis of prediction model performance allows researchers to obtain a summary estimate and investigate the presence of between-study heterogeneity from multiple estimates of prediction model performance.
Debray, T. P., Vergouwe, Y., Koffijberg, H., Nieboer, D., Steyerberg, E. W., & Moons, K. G. (2015). A new framework to enhance the interpretation of external validation studies of clinical prediction models. Journal of Clinical Epidemiology, 68(3), 279-289. https://dx.doi.org/10.1016/j.jclinepi.2014.06.018
Debray, T. P., Damen, J. A., et al. (2017). A guide to systematic review and meta-analysis of prediction model performance. BMJ, 356. https://doi.org/10.1136/bmj.i6460
Debray, T. P., Damen, et al. (2019). A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes. Statistical Methods in Medical Research, 28(9), 2768-2786. https://doi.org/10.1177/0962280218785504
Debray, T. P., Moons, K. G., & Riley, R. D. (2018). Detecting small-study effects and funnel plot asymmetry in meta‐analysis of survival data: a comparison of new and existing tests. Research Synthesis Methods, 9(1), 41-50. https://doi.org/10.1002/jrsm.1266
Debray, T. P. & de Jong, V. (2021). metamisc: Meta-Analysis of Diagnosis and Prognosis Research Studies. R package version 0.2.6/r591. https://R-Forge.R-project.org/projects/metamisc/
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