Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration

By Karel G. M. Moons, Douglas G. Altman, Johannes B Reitsma, J. Ioannidis, Petra Macaskill, Ewout W. Steyerberg, Andrew J. Vickers, D. Ransohoff, Gary S. Collins. 2015

4,311 citations recorded by the source corpus.

Subfields: Economics and Econometrics, General Health Professions, Statistics and Probability

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Citations in the corpus

Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators (2018, methodology): “Importantly, evaluating NB is recommended by the TRIPOD guidelines for prediction models [18].”
A new test and graphical tool to assess the goodness of fit of logistic regression models (2016, neutral): “frequencies across strata of risk [6].”
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.”
External validation of prognostic models: what, why, how, when and where? (2020, methodology): “For researchers planning an external validation study we highly recommend consulting the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis checklist and explanation and elaboration document [4].”
Sample size considerations for the external validation of a multivariable prognostic model: a resampling study (2015, background): “When validating a prognostic model, investigators should clearly explain how they determined their study size, so that their findings can be placed in context [17,18].”
Artificial Intelligence in Dentistry: Chances and Challenges (2020, methodology): “…and established in evidence-based medicine and dentistry; reporting the development and validation of any AI solution along the TRIPOD criteria (Moons et al. 2015); guarantying equitability and non-discrimination; retaining the responsibility with the healthcare professional, who in return…”
Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. (2020, neutral): “13”
Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement (2015, methodology): “The reporting of these studies fits entirely within the remit of TRIPOD (see accompanying explanation and elaboration document [43]).”