TRIPOD: A New Reporting Baseline for Developing and Interpreting Prediction Models
Authors: A. Russell Localio, Catharine B. Stack
Journal: Annals of Internal Medicine (2015)

Editorials6 January 2015TRIPOD: A New Reporting Baseline for Developing and Interpreting Prediction ModelsA. Russell Localio, PhD and Catharine B. Stack, PhD, Deputy Editor, StatisticsA. Russell Localio, PhDFrom Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, and Annals of Internal Medicine (Deputy Editor, Statistics). and Catharine B. Stack, PhD, Deputy Editor, StatisticsFrom Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, and Annals of Internal Medicine (Deputy Editor, Statistics).Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M14-2423 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail 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.... References1. Collins GS, Reitsma JB, Altman DG, Moons KG. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD statement. Ann Intern Med. 2015;162:55-63. doi:10.7326/M14-0697 LinkGoogle Scholar2. Moons KG, Altman DG, Reitsma JB, Ioannidis JP, Macaskill P, Steyerberg EW, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): explanation and elaboration. Ann Intern Med. 2015;162:W1-73. doi:10.7326/M14-0698 LinkGoogle Scholar3. Laine C, Goodman SN, Griswold ME, Sox HC. Reproducible research: moving toward research the public can really trust. Ann Intern Med. 2007;146:450-3. [PMID: 17339612] doi:10.7326/0003-4819-146-6-200703200-00154 LinkGoogle Scholar4. Smits M, Dippel DW, Steyerberg EW, deHaan GG, Dekker HM, Vos PE, et al. Predicting intracranial traumatic findings on computed tomography in patients with minor head injury: the CHIP prediction rule. Ann Intern Med. 2007;146:397-405. [PMID: 17371884] doi:10.7326/0003-4819-146-6-200703200-00004 LinkGoogle Scholar5. Lautenbach E, Localio R, Nachamkin I. Clinicians required very high sensitivity of a bacteremia prediction rule. J Clin Epidemiol. 2004;57:1104-6. [PMID: 15528062] CrossrefMedlineGoogle Scholar6. Vickers AJ, Cronin AM. Everything you always wanted to know about evaluating prediction models (but were too afraid to ask). Urology. 2010;76:1298-301. [PMID: 21030068] doi:10.1016/j.urology.2010.06.019 CrossrefMedlineGoogle Scholar7. Raji OY, Duffy SW, Agbaje OF, Baker SG, Christiani DC, Cassidy A, et al. Predictive accuracy of the Liverpool Lung Project risk model for stratifying patients for computed tomography screening for lung cancer: a case–control and cohort validation study. Ann Intern Med. 2012;157:242-50. [PMID: 22910935] doi:10.7326/0003-4819-157-4-201208210-00004 LinkGoogle Scholar8. Pepe MS, Janes H, Li CI. Net risk reclassification P values: valid or misleading? J Natl Cancer Inst. 2014;106:dju041. [PMID: 24681599] doi:10.1093/jnci/dju041 CrossrefMedlineGoogle Scholar9. Daniel RM, Cousens SN, DeStavola BL, Kenward MG, Sterne JA. Methods for dealing with time-dependent confounding. Stat Med. 2013;32:1584-618. [PMID: 23208861] doi:10.1002/sim.5686 CrossrefMedlineGoogle Scholar10. van Klaveren D, Steyerberg EW, Perel P, Vergouwe Y. Assessing discriminative ability of risk models in clustered data. BMC Med Res Methodol. 2014;14:5. [PMID: 24423445] doi:10.1186/1471-2288-14-5 CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAuthors: A. Russell Localio, PhD; Catharine B. Stack, PhD, Deputy Editor, StatisticsAffiliations: 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.Corresponding Author: 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. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoTransparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement Gary S. Collins , Johannes B. Reitsma , Douglas G. Altman , and Karel G.M. Moons Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration Karel G.M. Moons , Douglas G. Altman , Johannes B. Reitsma , John P.A. Ioannidis , Petra Macaskill , Ewout W. Steyerberg , Andrew J. Vickers , David F. Ransohoff , and Gary S. Collins Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement Gary S. Collins , Johannes B. Reitsma , Douglas G. Altman , and Karel G.M. Moons Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): Explanation and Elaboration Karel G.M. Moons , Douglas G. Altman , Johannes B. Reitsma , John P.A. Ioannidis , Petra Macaskill , Ewout W. Steyerberg , Andrew J. Vickers , David F. Ransohoff , and Gary S. Collins Metrics Cited byDelirium Prediction Using Machine Learning Interpretation Method and Its Incorporation into a Clinical WorkflowRadiomics-based prediction of two-year clinical outcome in locally advanced cervical cancer patients undergoing neoadjuvant chemoradiotherapyPrognostic Research in Traumatic Brain Injury: Markers, Modeling, and Methodological PrinciplesArtificial Intelligence and Liability in Medicine: Balancing Safety and InnovationScreening the risk factors for methamphetamine use in pregnant women not receiving prenatal careDevelopment and Validation of a Hospital Indicator of Activity-Based Costs for Injury AdmissionsCurrent Clinical Applications of Artificial Intelligence in Radiology and Their Best Supporting EvidenceRadiomics for radiation oncologists: are we ready to go?Oncotype DX Predictive Nomogram for Recurrence Score Output: The Novel System ADAPTED01 Based on Quantitative Immunochemistry AnalysisFunctional Outcome Prediction in Ischemic Stroke: A Comparison of Machine Learning Algorithms and Regression ModelsExternal Validation of Two Models to Predict Delirium in Critically Ill Adults Using Either the Confusion Assessment Method-ICU or the Intensive Care Delirium Screening Checklist for Delirium AssessmentMultinational development and validation of an early prediction model for delirium in ICU patients 6 January 2015Volume 162, Issue 1Page: 73-74KeywordsBacteremiaBiostatisticsCancer epidemiologyClinical epidemiologyDecision makingDisclosureInformation storage and retrievalMedical risk factorsStatistical dataStatistical methods ePublished: 6 January 2015 Issue Published: 6 January 2015 Copyright & PermissionsCopyright © 2015 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...