The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols | Nature Machine Intelligence
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The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols

Nature Machine Intelligence volume 5, pages 816–817 (2023)Cite this article

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Protocol development is a crucial step in mapping out any research study. It provides key details on the rationale, objectives, design, data collection, analysis methods and dissemination plans before a piece of research is conducted. Alongside study registration, protocols enhance transparency, replication and research integrity, and foster open-science principles. Developing a study protocol prompts a research team to consider and plan all steps in the study, provides guidance for all researchers involved in it and promotes good research practice.

Protocols are mandatory for some study designs, such as randomized trials (for example, for funding, ethical approval and submission to certain journals), and are increasingly encouraged for others, such as systematic reviews. The Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) statement1 provides guidance for clinical trial protocol reporting (with extensions recently developed for artificial intelligence (AI), SPIRIT-AI2), and the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) statement provides guidance for the reporting of systematic review protocols3. For many study designs, particularly those of an observational nature, there are conflicting opinions on the utility of protocols and study registration4. For example, it may not be possible, or desirable, to pre-specify all analyses, including when the study aim has an exploratory nature. However, even in such a case, developing and making a study protocol publicly available (albeit in broad terms) outlines intent and facilitates research transparency4,5.

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Authors and Affiliations

  1. Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology & Musculoskeletal Sciences, University of Oxford, Oxford, UK

    Paula Dhiman, Rebecca Whittle & Gary S. Collins

  2. Department of Development and Regeneration, KU Leuven, Leuven, Belgium

    Ben Van Calster

  3. Department of Electrical Engineering and Computer Science, Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA, USA

    Marzyeh Ghassemi

  4. University Hospitals Birmingham NHS Foundation Trust, Birmingham, UK

    Xiaoxuan Liu

  5. Department of Bioethics, Hospital for Sick Children, Toronto, Ontario, Canada

    Melissa D. McCradden

  6. Genetics & Genome Biology, Peter Gilgan Centre for Research and Learning, Division of Clinical and Public Health, Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada

    Melissa D. McCradden

  7. Julius Centre for Health Sciences and Primary Care, University Medical Centre Utrecht, Utrecht University, Utrecht, The Netherlands

    Karel G. M. Moons

  8. Institute of Applied Health Research, College of Medical and Dental Sciences, University of Birmingham, Birmingham, UK

    Richard D. Riley

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  1. Paula Dhiman
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  2. Rebecca Whittle
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  7. Karel G. M. Moons
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  9. Gary S. Collins
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Correspondence to Paula Dhiman.

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Dhiman, P., Whittle, R., Van Calster, B. et al. The TRIPOD-P reporting guideline for improving the integrity and transparency of predictive analytics in healthcare through study protocols. Nat Mach Intell 5, 816–817 (2023). https://doi.org/10.1038/s42256-023-00705-6

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