David M. Kent, MD, MS; Jessica K. Paulus, ScD; David van Klaveren, PhD; Ralph D'Agostino, PhD; Steve Goodman, MD, MHS, PhD; Rodney Hayward, MD; John P.A. Ioannidis, MD, DSc; Bray Patrick-Lake, MFS; Sally Morton, PhD; Michael Pencina, PhD; Gowri Raman, MBBS, MS; Joseph S. Ross, MD, MHS; Harry P. Selker, MD, MSPH; Ravi Varadhan, PhD; Andrew Vickers, PhD; John B. Wong, MD; Ewout W. Steyerberg, PhD
Disclaimer: The views, statements, and opinions presented in this work are solely the responsibility of the authors and do not necessarily represent the views of the Patient-Centered Outcomes Research Institute (PCORI), its Board of Governors, or its Methodology Committee.
Acknowledgment: The authors thank Mark Adkins, Teddy Balan, and Dan Sjoberg for excellent technical support in analyses included in the figures and supporting appendix tables. They also thank the Annals of Internal Medicine editors and reviewers, whose thoughtful feedback greatly improved this work. They thank Jennifer Lutz and Christine Lundquist for assistance with copyediting and creating exhibits.
Financial Support: Development of the PATH Statement was supported through contract SA.Tufts.PARC.OSCO.2018.01.25 from the PCORI Predictive Analytics Resource Center. This work was also informed by a 2018 conference (“Evidence and the Individual Patient: Understanding Heterogeneous Treatment Effects for Patient-Centered Care”) convened by the National Academy of Medicine and funded through a PCORI Eugene Washington Engagement Award (1900-TMC).
Disclosures: Dr. Kent reports grants from PCORI during the conduct of the study. Dr. Hayward reports grants from the National Institute of Diabetes and Digestive and Kidney Diseases and the Veterans Affairs Health Services Research and Development Service during the conduct of the study. Dr. Pencina reports grants from PCORI (Tufts Subaward) during the conduct of the study; grants from Sanofi/Regeneron, Amgen, and Bristol-Myers Squibb outside the submitted work; and personal fees from Boehringer Ingelheim and Merck outside the submitted work. Dr. Ross reports personal fees from PCORI during the conduct of the study and grants from the U.S. Food and Drug Administration, Medtronic, Johnson & Johnson, the Centers for Medicare & Medicaid Services, Blue Cross Blue Shield Association, the Agency for Healthcare Research and Quality, the National Institutes of Health (National Heart, Lung, and Blood Institute), and Laura and John Arnold Foundation outside the submitted work. Dr. Varadhan reports personal fees from Tufts University during the conduct of the study. Dr. Vickers reports grants from the National Institutes of Health during the conduct of the study. Dr. Wong reports grants from PCORI during the conduct of the study. Dr. Steyerberg reports royalties from Springer for his book Clinical Prediction Models. Authors not named here have disclosed no conflicts of interest. Disclosures can also be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M18-3667.
Corresponding Author: David M. Kent, MD, MS, Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, Box 63, Boston, MA 02111; e-mail, firstname.lastname@example.org.
Current Author Addresses: Drs. Kent, Paulus, Raman, and Selker: Predictive Analytics and Comparative Effectiveness (PACE) Center, Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, 800 Washington Street, Box 63, Boston, MA 02111.
Dr. van Klaveren: Erasmus University Medical Center, Doctor Molewaterplein 40, 3015 GD Rotterdam, the Netherlands.
Dr. D'Agostino: Boston University Mathematics and Statistics Department, 111 Cummington Street, Boston, MA 02215.
Dr. Goodman: Stanford University School of Medicine, 150 Governor's Lane, Room T265, Stanford, CA 94305.
Dr. Hayward: VA Ann Arbor Health Services Research and Development, 2800 Plymouth Road, Building 14, G100-36, Ann Arbor, MI 48109.
Dr. Ioannidis: Stanford Prevention Research Center, 1265 Welch Road, Stanford, CA 94305.
Ms. Patrick-Lake: Evidation Health, 167 2nd Avenue, San Mateo, CA 94401.
Dr. Morton: Virginia Tech, North End Center Suite 4300, 300 Turner Street NW, Blacksburg, VA 24061.
Dr. Pencina: Duke Clinical Research Institute, 200 Trent Street, Durham, NC 27710.
Dr. Ross: Yale University School of Medicine, PO Box 208093, New Haven, CT 06520.
Dr. Varadhan: Johns Hopkins University, Division of Biostatistics and Bioinformatics, 550 North Broadway, Suite 1103-A, Baltimore, MD 21205.
Dr. Vickers: Memorial Sloan Kettering Cancer Center, 485 Lexington Avenue, 2nd Floor, New York, NY 10017.
Dr. Wong: Tufts Medical Center, 800 Washington Street #302, Boston, MA 02111.
Dr. Steyerberg: Erasmus University Medical Center, PO Box 2040, 3055 PC Rotterdam, the Netherlands.
Author Contributions: Conception and design: D.M. Kent, J.K. Paulus, R. Hayward, J.P.A. Ioannidis, J.S. Ross, A. Vickers, J.B. Wong, E.W. Steyerberg.
Analysis and interpretation of the data: D.M. Kent, J.K. Paulus, R. D'Agostino, R. Hayward, J.P.A. Ioannidis, J.B. Wong, E.W. Steyerberg.
Drafting of the article: D.M. Kent, J.K. Paulus, R. D'Agostino, A. Vickers, J.B. Wong.
Critical revision of the article for important intellectual content: D.M. Kent, J.K. Paulus, D. van Klaveren, R. D'Agostino, R. Hayward, J.P.A. Ioannidis, S. Morton, M. Pencina, G. Raman, J.S. Ross, R. Varadhan, A. Vickers, J.B. Wong, E.W. Steyerberg.
Final approval of the article: D.M. Kent, J.K. Paulus, D. van Klaveren, R. D'Agostino, S. Goodman, R. Hayward, J.P.A. Ioannidis, B. Patrick-Lake, S. Morton, M. Pencina, G. Raman, J.S. Ross, H.P. Selker, R. Varadhan, A. Vickers, J.B. Wong, E.W. Steyerberg.
Provision of study materials or patients: D.M. Kent, J.B. Wong.
Statistical expertise: D.M. Kent, D. van Klaveren, R. D'Agostino, R. Hayward, J.P.A. Ioannidis, S. Morton, R. Varadhan, A. Vickers, J.B. Wong, E.W. Steyerberg.
Obtaining of funding: D.M. Kent, J.K. Paulus, J.B. Wong.
Administrative, technical, or logistic support: D.M. Kent, J.K. Paulus, G. Raman, H.P. Selker, J.B. Wong.
Collection and assembly of data: D.M. Kent, J.K. Paulus, G. Raman, J.B. Wong.
Heterogeneity of treatment effect (HTE) refers to the nonrandom variation in the magnitude or direction of a treatment effect across levels of a covariate, as measured on a selected scale, against a clinical outcome. In randomized controlled trials (RCTs), HTE is typically examined through a subgroup analysis that contrasts effects in groups of patients defined “1 variable at a time” (for example, male vs. female or old vs. young). The authors of this statement present guidance on an alternative approach to HTE analysis, “predictive HTE analysis.” The goal of predictive HTE analysis is to provide patient-centered estimates of outcome risks with versus without the intervention, taking into account all relevant patient attributes simultaneously. The PATH (Predictive Approaches to Treatment effect Heterogeneity) Statement was developed using a multidisciplinary technical expert panel, targeted literature reviews, simulations to characterize potential problems with predictive approaches, and a deliberative process engaging the expert panel. The authors distinguish 2 categories of predictive HTE approaches: a “risk-modeling” approach, wherein a multivariable model predicts the risk for an outcome and is applied to disaggregate patients within RCTs to define risk-based variation in benefit, and an “effect-modeling” approach, wherein a model is developed on RCT data by incorporating a term for treatment assignment and interactions between treatment and baseline covariates. Both approaches can be used to predict differential absolute treatment effects, the most relevant scale for clinical decision making. The authors developed 4 sets of guidance: criteria to determine when risk-modeling approaches are likely to identify clinically important HTE, methodological aspects of risk-modeling methods, considerations for translation to clinical practice, and considerations and caveats in the use of effect-modeling approaches. The PATH Statement, together with its explanation and elaboration document, may guide future analyses and reporting of RCTs.
Kent DM, Paulus JK, van Klaveren D, et al. The Predictive Approaches to Treatment effect Heterogeneity (PATH) Statement. Ann Intern Med. 2019;:. [Epub ahead of print 12 November 2019]. doi: https://doi.org/10.7326/M18-3667
Download citation file:
Published: Ann Intern Med. 2019.
Research and Reporting Methods.
Results provided by:
Copyright © 2019 American College of Physicians. All Rights Reserved.
Print ISSN: 0003-4819 | Online ISSN: 1539-3704
Conditions of Use