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Assessment of heterogeneity in an individual participant data meta‐analysis of prediction models: An overview and illustration

Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta‐analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictio...

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Detalles Bibliográficos
Autores principales: Steyerberg, Ewout W., Nieboer, Daan, Debray, Thomas P.A., van Houwelingen, Hans C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6772012/
https://www.ncbi.nlm.nih.gov/pubmed/31373722
http://dx.doi.org/10.1002/sim.8296
Descripción
Sumario:Clinical prediction models aim to provide estimates of absolute risk for a diagnostic or prognostic endpoint. Such models may be derived from data from various studies in the context of a meta‐analysis. We describe and propose approaches for assessing heterogeneity in predictor effects and predictions arising from models based on data from different sources. These methods are illustrated in a case study with patients suffering from traumatic brain injury, where we aim to predict 6‐month mortality based on individual patient data using meta‐analytic techniques (15 studies, n = 11 022 patients). The insights into various aspects of heterogeneity are important to develop better models and understand problems with the transportability of absolute risk predictions.