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CC BY-NC V4.0, Open Access
Journal article
A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
Statistical methods in medical research, Vol.28(9), pp.2768-2786
09/2019
Handle:
https://hdl.handle.net/2376/105866
PMCID: PMC6728752
PMID: 30032705
Abstract
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”.
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Details
- Title
- A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes
- Creators
- Thomas PA Debray - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The NetherlandsJohanna AAG Damen - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The NetherlandsRichard D Riley - Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire, UKKym Snell - Research Institute for Primary Care and Health Sciences, Keele University, Staffordshire, UKJohannes B Reitsma - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The NetherlandsLotty Hooft - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The NetherlandsGary S Collins - Centre for Statistics in Medicine, University of Oxford, Oxford, UKKarel GM Moons - Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
- Publication Details
- Statistical methods in medical research, Vol.28(9), pp.2768-2786
- Academic Unit
- Department of Physics and Astronomy
- Publisher
- SAGE Publications; Sage UK: London, England
- Grant note
- MTH001F / ; 91617050 / ;
- Identifiers
- 99900546509701842
- Language
- English
- Resource Type
- Journal article