Sanjay Basu, MD, PhD; Jeremy B. Sussman, MD, MS; Rod A. Hayward, MD
Disclaimer: The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the U.S. Department of Veterans Affairs.
Grant Support: By the National Institutes of Health (awards DP2MD010478, U54MD010724, K08HL121056, and P60DK20572) and the U.S. Department of Veterans Affairs Health Services Research & Development Service (award IIR02225).
Disclosures: Authors have disclosed no conflicts of interest. Forms can be viewed at www.acponline.org/authors/icmje/ConflictOfInterestForms.do?msNum=M16-1756.
Editors' Disclosures: Christine Laine, MD, MPH, Editor in Chief, reports that she has no financial relationships or interests to disclose. Darren B. Taichman, MD, PhD, Executive Deputy Editor, reports that he has no financial relationships or interests to disclose. Cynthia D. Mulrow, MD, MSc, Senior Deputy Editor, reports that she has no relationships or interests to disclose. Deborah Cotton, MD, MPH, Deputy Editor, reports that she has no financial relationships or interest to disclose. Jaya K. Rao, MD, MHS, Deputy Editor, reports that she has stock holdings/options in Eli Lilly and Pfizer. Sankey V. Williams, MD, Deputy Editor, reports that he has no financial relationships or interests to disclose. Catharine B. Stack, PhD, MS, Deputy Editor for Statistics, reports that she has stock holdings in Pfizer and Johnson & Johnson.
Reproducible Research Statement:Study protocol: Not applicable. Statistical code and data set: Available at sdr.stanford.edu.
Requests for Single Reprints: Sanjay Basu, MD, PhD, Center for Population Health Sciences, Stanford University School of Medicine, 1070 Arastradero Road 282, MC 5560, Palo Alto, CA 94304; e-mail, firstname.lastname@example.org.
Current Author Addresses: Dr. Basu: Center for Population Health Sciences, Stanford University School of Medicine, 1070 Arastradero Road 282, MC 5560, Palo Alto, CA 94304.
Dr. Sussman: University of Michigan, 2800 Plymouth Road, Building 16, Room 335E, Ann Arbor, MI 48109.
Dr. Hayward: University of Michigan, 2800 Plymouth Road, Building 10, Room G016-4A, Ann Arbor, MI 48109.
Author Contributions: Conception and design: J.B. Sussman, R.A. Hayward.
Analysis and interpretation of the data: S. Basu, R.A. Hayward.
Drafting of the article: S. Basu.
Critical revision of the article for important intellectual content: J.B. Sussman, R.A. Hayward.
Final approval of the article: S. Basu, J.B. Sussman, R.A. Hayward.
Statistical expertise: R.A. Hayward.
Administrative, technical, or logistic support: R.A. Hayward.
Two recent randomized trials produced discordant results when testing the benefits and harms of treatment to reduce blood pressure (BP) in patients with cardiovascular disease (CVD).
To perform a theoretical modeling study to identify whether large, clinically important differences in benefit and harm among patients (heterogeneous treatment effects [HTEs]) can be hidden in, and explain discordant results between, treat-to-target BP trials.
Results of 2 trials comparing standard (systolic BP target <140 mm Hg) with intensive (systolic BP target <120 mm Hg) BP treatment and data from the National Health and Nutrition Examination Survey (2013 to 2014).
CVD events and mortality.
Clinically important HTEs could explain differences in outcomes between 2 trials of intensive BP treatment, particularly diminishing benefit with each additional BP agent (for example, adding a second agent reduces CVD risk [hazard ratio, 0.61], but adding a fourth agent to a third has no benefit) and increasing harm at low diastolic BP.
Conventional treat-to-target trial designs had poor (<5%) statistical power to detect the HTEs, despite large samples (n > 20 000), and produced biased effect estimates. In contrast, a trial with sequential randomization to more intensive therapy achieved greater than 80% power and unbiased HTE estimates, despite small samples (n = 3500).
The HTEs as a function of the number of BP agents only were explored. Simulated aggregate data from the trials were used as model inputs because individual-participant data were not available.
Clinically important heterogeneity in intensive BP treatment effects remains undetectable in conventional trial designs but can be detected in sequential randomization trial designs.
National Institutes of Health and U.S. Department of Veterans Affairs.
Basu S, Sussman JB, Hayward RA. Detecting Heterogeneous Treatment Effects to Guide Personalized Blood Pressure Treatment: A Modeling Study of Randomized Clinical Trials. Ann Intern Med. [Epub ahead of print 3 January 2017]166:354–360. doi: 10.7326/M16-1756
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Published: Ann Intern Med. 2017;166(5):354-360.
Published at www.annals.org on 3 January 2017
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