Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration

BMJ, vol. 380, pp. e071058

Abstract

To evaluate whether a prediction model is fit for purpose, and to properly assess their quality and any risks of bias, full and transparent reporting of prediction model studies is essential TRIPOD-Cluster is a new reporting checklist for prediction model studies that are based on clustered datasets Clustered datasets can be obtained by combining individual participant data from multiple studies, by conducting multicentre studies, or by retrieving individual participant data from registries or datasets with electronic health records; presence of clustering can lead to differences (or heterogeneity) between clusters regarding participant characteristics, baseline risk, predictor effects, and outcome occurrence Performance of prediction models can vary across clusters, and thereby affect their generalisability Additional reporting efforts are needed in clustered data to clarify the identification of eligible data sources, data preparation, risk-of-bias assessment, heterogeneity in prediction model parameters, and heterogeneity in prediction model performance estimates

Authors 7

1. Thomas P. A. Debray

   Utrecht University · University Medical Center Utrecht

   Affiliation as printed

   Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands

   Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands

2. Professor Gary S. Collins

   John Radcliffe Hospital · National Institute for Health and Care Research · University of Oxford

   Affiliation as printed

   Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, Botnar Research Centre, University of Oxford, Oxford, UK

   National Institute for Health and Care Research Oxford Biomedical Research Centre, John Radcliffe Hospital, Oxford, UK

3. Richard D Riley

   Keele University

   Affiliation as printed

   Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK

4. Kym I E Snell

   Keele University

   Affiliation as printed

   Centre for Prognosis Research, School of Medicine, Keele University, Keele, UK

5. Ben Van Calster Aachen Department of Biomedical Data Sciences

   Leiden University Medical Center · KU Leuven

   Affiliation as printed

   Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, Netherlands

   Department of Development and Regeneration, KU Leuven, Leuven, Belgium

   EPI-centre, KU Leuven, Leuven, Belgium

6. Johannes B. Reitsma

   Utrecht University · University Medical Center Utrecht

   Affiliation as printed

   Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands

   Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands

7. Karel G.M. Moons

   Utrecht University · University Medical Center Utrecht

   Affiliation as printed

   Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands

   Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands