Bryan R Luce1, Jason T Connor1, Kristine R Broglio1, C Daniel Mullins1, K Jack Ishak1, Elijah Saunders1, Barry R Davis1. 1. From the University of Washington, Seattle, Washington; Berry Consultants, Miami, Florida; University of Central Florida College of Medicine, Orlando, Florida; Berry Consultants, Austin, Texas; University of Maryland School of Pharmacy and School of Medicine, Baltimore, Maryland; Evidera, Montreal, Quebec, Canada, and Bethesda, Maryland; and University of Texas School of Public Health, Houston, Texas.
Abstract
BACKGROUND: Bayesian and adaptive clinical trial designs offer the potential for more efficient processes that result in lower sample sizes and shorter trial durations than traditional designs. OBJECTIVE: To explore the use and potential benefits of Bayesian adaptive clinical trial designs in comparative effectiveness research. DESIGN: Virtual execution of ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) as if it had been done according to a Bayesian adaptive trial design. SETTING: Comparative effectiveness trial of antihypertensive medications. PATIENTS: Patient data sampled from the more than 42 000 patients enrolled in ALLHAT with publicly available data. MEASUREMENTS: Number of patients randomly assigned between groups, trial duration, observed numbers of events, and overall trial results and conclusions. RESULTS: The Bayesian adaptive approach and original design yielded similar overall trial conclusions. The Bayesian adaptive trial randomly assigned more patients to the better-performing group and would probably have ended slightly earlier. LIMITATIONS: This virtual trial execution required limited resampling of ALLHAT patients for inclusion in RE-ADAPT (REsearch in ADAptive methods for Pragmatic Trials). Involvement of a data monitoring committee and other trial logistics were not considered. CONCLUSION: In a comparative effectiveness research trial, Bayesian adaptive trial designs are a feasible approach and potentially generate earlier results and allocate more patients to better-performing groups. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute.
RCT Entities:
BACKGROUND: Bayesian and adaptive clinical trial designs offer the potential for more efficient processes that result in lower sample sizes and shorter trial durations than traditional designs. OBJECTIVE: To explore the use and potential benefits of Bayesian adaptive clinical trial designs in comparative effectiveness research. DESIGN: Virtual execution of ALLHAT (Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial) as if it had been done according to a Bayesian adaptive trial design. SETTING: Comparative effectiveness trial of antihypertensive medications. PATIENTS: Patient data sampled from the more than 42 000 patients enrolled in ALLHAT with publicly available data. MEASUREMENTS: Number of patients randomly assigned between groups, trial duration, observed numbers of events, and overall trial results and conclusions. RESULTS: The Bayesian adaptive approach and original design yielded similar overall trial conclusions. The Bayesian adaptive trial randomly assigned more patients to the better-performing group and would probably have ended slightly earlier. LIMITATIONS: This virtual trial execution required limited resampling of ALLHAT patients for inclusion in RE-ADAPT (REsearch in ADAptive methods for Pragmatic Trials). Involvement of a data monitoring committee and other trial logistics were not considered. CONCLUSION: In a comparative effectiveness research trial, Bayesian adaptive trial designs are a feasible approach and potentially generate earlier results and allocate more patients to better-performing groups. PRIMARY FUNDING SOURCE: National Heart, Lung, and Blood Institute.
Authors: Kristine Broglio; William J Meurer; Valerie Durkalski; Qi Pauls; Jason Connor; Donald Berry; Roger J Lewis; Karen C Johnston; William G Barsan Journal: JAMA Netw Open Date: 2022-05-02
Authors: Elizabeth G Ryan; Julie Bruce; Andrew J Metcalfe; Nigel Stallard; Sarah E Lamb; Kert Viele; Duncan Young; Simon Gates Journal: BMC Med Res Methodol Date: 2019-05-14 Impact factor: 4.612