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Introduction <p>The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement was published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-Code) for the management of code associated with prediction model studies. TRIPOD-Code focuses specifically on the transparent reporting of analytical code used in prediction model studies, including code for data preprocessing, model development, and model evaluation.</p> Methods and analysis <p>TRIPOD-Code will be developed following published guidance from the EQUATOR Network and will comprise five stages. Stage 1 will involve a methodological review of how code availability is reported in published prediction model studies. In Stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-Code. Stage 3 will consist of virtual consensus meetings to consolidate and prioritise the key items. Stage 4 will involve developing the TRIPOD-Code checklist. In the final stage, Stage 5, we will disseminate TRIPOD-Code via journals, conferences, blogs, websites (including TRIPOD and the EQUATOR Network) and social media. TRIPOD-Code will provide researchers working on prediction model studies with a reporting checklist and accompanying guidance to promote code completeness and availability.</p> Ethics and dissemination <p>This study has been determined to be exempt from ongoing IRB oversight by the Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects (COUHES) under Exempt ID: E-6675. Findings from this study will be disseminated through peer-reviewed publications.</p>

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Protocol for development of a reporting guideline (TRIPOD-Code) for code repositories associated with diagnostic and prognostic prediction model studies

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月10日

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  • Tom Pollard,
  • Thomas Sounack,
  • Catherine A. Gao,
  • Leo Anthony Celi,
  • Charlotta Lindvall,
  • Hyeonhoon Lee,
  • Hyung-Chul Lee,
  • Karel G. M. Moons,
  • Gary S. Collins

摘要

Introduction

The Transparent Reporting of a multivariable prediction model of Individual Prognosis Or Diagnosis (TRIPOD) statement was published to improve the reporting and critical appraisal of prediction model studies for diagnosis and prognosis. This paper describes the processes and methods that will be used to develop an extension to the TRIPOD statement (TRIPOD-Code) for the management of code associated with prediction model studies. TRIPOD-Code focuses specifically on the transparent reporting of analytical code used in prediction model studies, including code for data preprocessing, model development, and model evaluation.

Methods and analysis

TRIPOD-Code will be developed following published guidance from the EQUATOR Network and will comprise five stages. Stage 1 will involve a methodological review of how code availability is reported in published prediction model studies. In Stage 2, we will consult a diverse group of key stakeholders using a Delphi process to identify items to be considered for inclusion in TRIPOD-Code. Stage 3 will consist of virtual consensus meetings to consolidate and prioritise the key items. Stage 4 will involve developing the TRIPOD-Code checklist. In the final stage, Stage 5, we will disseminate TRIPOD-Code via journals, conferences, blogs, websites (including TRIPOD and the EQUATOR Network) and social media. TRIPOD-Code will provide researchers working on prediction model studies with a reporting checklist and accompanying guidance to promote code completeness and availability.

Ethics and dissemination

This study has been determined to be exempt from ongoing IRB oversight by the Massachusetts Institute of Technology Committee on the Use of Humans as Experimental Subjects (COUHES) under Exempt ID: E-6675. Findings from this study will be disseminated through peer-reviewed publications.