Using Bayesian Adaptive Trial Designs for Comparative Effectiveness Research: A Virtual Trial Execution

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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.

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