A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes.
Stat Methods Med Res
; 28(9): 2768-2786, 2019 09.
Article
en En
| MEDLINE
| ID: mdl-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 observedexpected 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".
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
Proyectos de Investigación
/
Metaanálisis como Asunto
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Modelos Estadísticos
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Medición de Riesgo
Tipo de estudio:
Etiology_studies
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Prognostic_studies
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Risk_factors_studies
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Systematic_reviews
Límite:
Humans
Idioma:
En
Revista:
Stat Methods Med Res
Año:
2019
Tipo del documento:
Article
País de afiliación:
Países Bajos