Abstract

We propose a class of adaptive randomized trial designs for comparing two treatments to a common control in two disjoint subpopulations. The type of adaptation, called adaptive enrichment, involves a preplanned rule for modifying enrollment and arm assignment based on accruing data in an ongoing trial. The motivation for this adaptive feature is that interim data may indicate that a subpopulation, such as those with lower disease severity at baseline, are unlikely to benefit from a particular treatment, while uncertainty remains for the other treatment and/or subpopulation. We developed a new multiple testing procedure tailored to this design problem. The procedure improves power by: leveraging the correlation between the test statistics arising from the two treatments being compared to a common control; reallocating alpha across subpopulations, and using the data only through minimally sufficient statistics. We optimize expected sample size over this class of designs, focusing on designs with 2 stages. Our approach is demonstrated in simulation studies that mimic features of a completed trial of a medical device for treating heart failure. User-friendly, open-source software that implements the trial design optimization is provided.

Disciplines

Biostatistics | Statistical Methodology

Media Format

flash_audio

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