Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement

By Gary S. Collins, Johannes B Reitsma, Douglas G. Altman, Karel G. M. Moons. 2015

899 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Family Practice, Statistics and Probability

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Citations in the corpus

"The human body is a black box": supporting clinical decision-making with deep learning (2019, positive): “The deep learning model was published and reported according to the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines [11].”
Using clinical Natural Language Processing for health outcomes research: Overview and actionable suggestions for future advances (2018, positive): “Validation or evaluation of clinical outcomes whether it be a trial, cohort or case-control study relies on statistical measurements of effect, and can be validated internally (measured on the original study sample) or externally (measured on a different sample) [31].”
Cross-Sectional Studies: Strengths, Weaknesses, and Recommendations. (2020, methodology): “S70 Supplement When diagnostic prediction models are developed in a cross-sectional study, we recommend that investigators follow the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement.(24) The statement is a checklist of 22 items deeme”
A validation of machine learning-based risk scores in the prehospital setting (2019, methodology): “To enhance reproducibility, we sought to adhere to TRIPOD guidelines in reporting our results regarding the development and validation of these models [50], and it is hoped that the source code found in S1 Code will facilitate the replication of-, and improvement upon our results.”
Benefit and harm of intensive blood pressure treatment: Derivation and validation of risk models using data from the SPRINT and ACCORD trials (2017, positive): “Study design and reporting was based on the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) Statement [14].”
Supervised machine learning: A brief primer (2020, methodology): “…also should follow standard reporting guidelines such as the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement (Collins et al., 2015), which are currently being adapted for machine learning applications (Collins & Moons, 2019).”