Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist

By Karel G. M. Moons, Joris A. H. de Groot, W. Bouwmeester, Y. Vergouwe, S. Mallett, Douglas G. Altman, Johannes B Reitsma, Gary S. Collins. PLoS Medicine, 2014

1,708 citations recorded by the source corpus.

Carl Moons and colleagues provide a checklist and background explanation for critically appraising and extracting data from systematic reviews of prognostic and diagnostic prediction modelling studies. Please see later in the article for the Editors' Summary

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/c79bfa9a67c8745d7620fcfe8cf582b9d0407073

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Citations in the corpus

Sample size for binary logistic prediction models: Beyond events per variable criteria (2018, methodology): “This minimal sample size criterion has also generally been accepted as a methodological quality item in appraising published prediction modeling studies.(2,14,16) However, some authors have expressed concerns that that the EPV 10 rule-of-thumb is not based on convincing scientific reasoning.”
PROBAST+AI: an updated quality, risk of bias, and applicability assessment tool for prediction models using regression or artificial intelligence methods (2025, positive): “…intended use of the prediction model) criteria as provided by the guidance of the Cochrane Prognosis Methods group (https://methods.cochrane.org/ prognosis/) and described in CHARMS (checklist for critical appraisal and data extraction for systematic reviews of prediction modelling studies 61 ).”
PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies (2019, neutral): “Assessors are first asked to report their systematic review question based on the guidance given in the CHARMS checklist.(41)”
Effect of Ebola Progression in Liberia (2015, neutral): “The TRIPOD Statement does not prescribe how studies developing, validating, or updating prediction models should be undertaken, nor should it be used as a tool for explicitly assessing quality or quantifying risk of bias in such studies (52).”
A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. (2019, methodology): “Definitions of what constitutes ML and the differences with statistical modeling have been discussed at length in the literature [8], yet the distinction is not clear-cut [9].”
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement (2015, methodology): “The TRIPOD Statement does not prescribe how studies developing, validating, or updating prediction models should be undertaken, nor should it be used as a tool for explicitly assessing quality or quantifying risk of bias in such studies [52].”
Effect of Ebola Progression in Liberia (2015, neutral): “The TRIPOD Statement does not prescribe how studies developing, validating, or updating prediction models should be undertaken, nor should it be used as a tool for explicitly assessing quality or quantifying risk of bias in such studies.(52) There is, however, an implicit expectation that authors ha”
What kind of systematic review should I conduct? A proposed typology and guidance for systematic reviewers in the medical and health sciences (2018, background): “Checklist) [38] have been developed to assist in this process (see Table 1).”
Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement (2015, neutral): “The TRIPOD Statement does not prescribe how studies developing, validating, or updating prediction models should be undertaken, nor should it be used as a tool for explicitly assessing quality or quantifying risk of bias in such studies (52).”