Debray, TPA, Collins, GS, Riley, RD, Snell, KIE, Van Calster, B, Reitsma, JB and Moons, KGM (2023) Transparent reporting of multivariable prediction models developed or validated using clustered data: TRIPOD-Cluster checklist. BMJ, 380. ISSN 0959-535X

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Abstract

The increasing availability of large combined datasets (or big data), such as those from electronic health records and from individual participant data meta-analyses, provides new opportunities and challenges for researchers developing and validating (including updating) prediction models. These datasets typically include individuals from multiple clusters (such as multiple centres, geographical locations, or different studies). Accounting for clustering is important to avoid misleading conclusions and enables researchers to explore heterogeneity in prediction model performance across multiple centres, regions, or countries, to better tailor or match them to these different clusters, and thus to develop prediction models that are more generalisable. However, this requires prediction model researchers to adopt more specific design, analysis, and reporting methods than standard prediction model studies that do not have any inherent substantial clustering. Therefore, prediction model studies based on clustered data need to be reported differently so that readers can appraise the study methods and findings, further increasing the use and implementation of such prediction models developed or validated from clustered datasets.

Item Type: Article
Additional Information: This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/.
Subjects: R Medicine > R Medicine (General)
Divisions: Faculty of Medicine and Health Sciences > School of Primary, Community and Social Care
Related URLs:
Depositing User: Symplectic
Date Deposited: 16 Mar 2023 14:47
Last Modified: 16 Mar 2023 14:47
URI: https://eprints.keele.ac.uk/id/eprint/12026

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