Transparent reporting of multivariable prediction models developed or validated using clustered data (TRIPOD-Cluster): explanation and elaboration
Thomas P. A. Debray, Gary S. Collins, Richard D Riley, Kym I E Snell, Ben Van Calster, Johannes B. Reitsma, Karel G.M. Moons
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 on 20 July