Detecting Heterogeneous Treatment Effects to Guide Personalized Blood Pressure Treatment

Abstract

Background 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). Objective 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. Design Microsimulation. Data Sources 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). Target Population U.S. adults. Time Horizon 5 years. Perspective Societal. Intervention BP treatment. Outcome Measures CVD events and mortality. Results of Base-Case Analysis 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. Results of Sensitivity Analysis 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). Limitations 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. Conclusion Clinically important heterogeneity in intensive BP treatment effects remains undetectable in conventional trial designs but can be detected in sequential randomization trial designs. Primary Funding Source National Institutes of Health and U.S. Department of Veterans Affairs.

Development and validation of Risk Equations for Complications Of type 2 Diabetes (RECODe) using individual participant data from randomised trials

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