Advancing Personalized Medicine Through Prediction

Editorials7 January 2020Advancing Personalized Medicine Through PredictionA. Russell Localio, PhD, Cynthia D. Mulrow, MD, MSc, and Michael E. Griswold, PhDA. Russell Localio, PhDPerelman School of Medicine, University of Pennsylvania Philadelphia, Pennsylvania (A.R.L.), Cynthia D. Mulrow, MD, MScSenior Deputy Editor, Annals of Internal Medicine Philadelphia, Pennsylvania (C.D.M.), and Michael E. Griswold, PhDMIND Center, University of Mississippi Medical Center Jackson, Mississippi (M.E.G.)Author, Article, and Disclosure Informationhttps://doi.org/10.7326/M19-3010 SectionsAboutFull TextPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinkedInRedditEmail Personalized medicine is the tailoring of medical treatment to each individual patient. Everyone wants it; clinicians aim to deliver it. We rely on randomized trials for evidence of treatment effects, but randomized trials typically report “overall” benefits and harms averaged across heterogeneous patients. This practice might facilitate medicine, but not personalized medicine. The traditional next step toward personalizing trial results has been subgroup analyses, but these are fraught with false-negative and false-positive results (1). Current approaches for trial reporting often fail to inform the individual patient and his or her physician of the best treatment choice.In their articles on ...References1. Lanza ST, Rhoades BL. Latent class analysis: an alternative perspective on subgroup analysis in prevention and treatment. Prev Sci. 2013;14:157-68. [PMID: 21318625] doi:10.1007/s11121-011-0201-1 CrossrefMedlineGoogle Scholar2. Kent DM, Paulus JK, van Klaveren D, et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) statement. Ann Intern Med. 2020;172:35-45. doi:10.7326/M18-3667 LinkGoogle Scholar3. Kent DM, van Klaveren D, Paulus JK, et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) statement: explanation and elaboration. Ann Intern Med. 2020;172:W1-25. doi:10.7326/M18-3668 LinkGoogle Scholar4. Schwartz LM, Woloshin S, Dvorin EL, et al. Ratio measures in leading medical journals: structured review of accessibility of underlying absolute risks. BMJ. 2006;333:1248. [PMID: 17060338] CrossrefMedlineGoogle Scholar5. Laine C, Goodman SN, Griswold ME, et al. Reproducible research: moving toward research the public can really trust. Ann Intern Med. 2007;146:450-3. [PMID: 17339612] LinkGoogle Scholar6. Vickers AJ, Elkin EB. Decision curve analysis: a novel method for evaluating prediction models. Med Decis Making. 2006;26:565-74. [PMID: 17099194] CrossrefMedlineGoogle Scholar7. Janes H, Pepe MS, Bossuyt PM, et al. Measuring the performance of markers for guiding treatment decisions. Ann Intern Med. 2011;154:253-9. [PMID: 21320940]. doi:10.7326/0003-4819-15-44-201102150-00006 LinkGoogle Scholar8. Senn S. Statistical pitfalls of personalized medicine. Nature. 2018;563:619-21. [PMID: 30482931] doi:10.1038/d41586-018-07535-2 CrossrefMedlineGoogle Scholar9. Raji OY, Duffy SW, Agbaje OF, 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 Scholar10. VanderWeele TJ, Robins JM. Four types of effect modification: a classification based on directed acyclic graphs. Epidemiology. 2007;18:561-8. [PMID: 17700242] CrossrefMedlineGoogle Scholar Author, Article, and Disclosure InformationAffiliations: Perelman School of Medicine, University of Pennsylvania Philadelphia, Pennsylvania (A.R.L.)Senior Deputy Editor, Annals of Internal Medicine Philadelphia, Pennsylvania (C.D.M.)MIND Center, University of Mississippi Medical Center Jackson, Mississippi (M.E.G.)Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M19-3010.Corresponding Author: A. Russell Localio, PhD, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 617 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021; e-mail, [email protected]upenn.edu.Current Author Addresses: Dr. Localio: Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, 617 Blockley Hall, 423 Guardian Drive, Philadelphia, PA 19104-6021.Dr. Mulrow: American College of Physicians, 190 N. Independence Mall West, Philadelphia, PA 19106.Dr. Griswold: University of Mississippi Medical Center, 2500 North State Street, Jackson, MS 39216.This article was published at Annals.org on 12 November 2019. PreviousarticleNextarticle Advertisement FiguresReferencesRelatedDetailsSee AlsoThe Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement David M. Kent , Jessica K. Paulus , David van Klaveren , Ralph D'Agostino , Steve Goodman , Rodney Hayward , John P.A. Ioannidis , Bray Patrick-Lake , Sally Morton , Michael Pencina , Gowri Raman , Joseph S. Ross , Harry P. Selker , Ravi Varadhan , Andrew Vickers , John B. Wong , and Ewout W. Steyerberg The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement: Explanation and Elaboration David M. Kent , David van Klaveren , Jessica K. Paulus , Ralph D'Agostino , Steve Goodman , Rodney Hayward , John P.A. Ioannidis , Bray Patrick-Lake , Sally Morton , Michael Pencina , Gowri Raman , Joseph S. Ross , Harry P. Selker , Ravi Varadhan , Andrew Vickers , John B. Wong , and Ewout W. Steyerberg Metrics Cited byMolecular diagnostics and targeted treatment approaches in pediatric oncology (literature review) 7 January 2020Volume 172, Issue 1Page: 63-64KeywordsBiostatisticsClinical trialsDecision makingEpidemiologyMedical risk factorsPrecision medicineRandomized trialsStrokeTraditional medicineTreatment guidelines ePublished: 12 November 2019 Issue Published: 7 January 2020 Copyright & PermissionsCopyright © 2019 by American College of Physicians. All Rights Reserved.PDF downloadLoading ...