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Editorials |6 January 2015

TRIPOD: A New Reporting Baseline for Developing and Interpreting Prediction Models

A. Russell Localio, PhD; Catharine B. Stack, PhD, Deputy Editor, Statistics

Article, Author, and Disclosure Information
Author, Article, and Disclosure Information
  • From Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, and Annals of Internal Medicine (Deputy Editor, Statistics).

    Disclosures: Disclosures can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M14-2423.

    Requests for Single Reprints: A. Russell Localio, PhD, Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, 635 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021.

    Current Author Addresses: Dr. Localio: Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, Perelman School of Medicine, University of Pennsylvania, 635 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021.

    Dr. Stack: American College of Physicians, 190 N. Independence Mall West, Philadelphia, PA 19106.

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In this issue, the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) investigators propose an annotated checklist for transparent reporting of prediction or prognostic models (1). The accompanying 22 000-word “Explanation and Elaboration” (2), with more than 500 references from statistics, epidemiology, and clinical decision making as well as from the applied clinical literature, should serve as an important resource for model developers. Applications span prediction (for diagnosis from cross-sectional data) and prognosis (for outcomes after longitudinal follow-up) across many clinical settings. These TRIPOD documents represent serious efforts to synthesize best practices for authors and readers.
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Localio AR, Stack CB. TRIPOD: A New Reporting Baseline for Developing and Interpreting Prediction Models. Ann Intern Med. ;162:73–74. doi: 10.7326/M14-2423

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Published: Ann Intern Med. 2015;162(1):73-74.

DOI: 10.7326/M14-2423

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