BACKGROUND

Standard clinical trial designs aim to determine whether a treatment is beneficial, on average, for a target population. Such trials can have low power if the treatment only benefits a subpopulation, for example, that adaptively change enrollment criteria during a trial, called adaptive enrichment designs, have potential to provide improved information about which subpopulations benefit from new treatments.

OBJECTIVES

We aimed to (i) develop new adaptive enrichment designs and prove their key statistical properties; (ii) conduct simulations that mimic features of completed trial data sets in order to evaluate the new trial designs’ performance (such as sample size, duration, power, bias); the data sets are from trials involving treatments for HIV, stroke, and heart failure; (iii) develop user-friendly software for optimizing the performance of our new adaptive designs and comparing them to standard designs. The goal was to construct designs that satisfy power and type I error requirements at the minimum cost in terms of expected sample size, ie, average sample size over a set of plausible scenarios. We also considered the maximum sample size, ie, the number of participants enrolled if there is no early stopping.

METHODS

We constructed new adaptive trial designs (including new rules for modifying enrollment and new procedures for testing multiple hypotheses) and proved key statistical properties such as control of the study-wide type I error rate.

RESULTS

For the simulation study involving stroke, the new adaptive design reduced expected sample size by 32% compared with standard designs; the tradeoff is that the maximum sample size was 22% larger for the adaptive design. For the simulation study involving the cardiac resynchronization device for treating heart failure, the benefit of the adaptive design was a 25% reduction in expected sample size but an 8% increase in maximum sample size vs standard designs. For the simulation study involving HIV, the adaptive designs did not provide substantial benefits.

CONCLUSIONS

Optimized, adaptive enrichment designs can lead to reduced expected sample size compared with standard designs, in some settings. For adaptive enrichment to substantially add value, a sufficient number of primary outcomes need to be observed before enrollment is exhausted; this depends on the enrollment rate and the time from enrollment to observation of the primary outcome. Adaptive designs often involve tradeoffs such as reduced expected sample size at the price of greater maximum sample size, compared with standard designs. Our software can reveal these trade-offs and determine whether certain adaptive enrichment designs substantially add value for a given trial design problem; this enables trial statisticians to make informed decisions among trial design options. Our designs assumed that subpopulations are defined before the trial starts, which requires prior data and scientific understanding of who may be more likely to benefit from the treatment. The sample size required to determine treatment effects for subpopulations can be substantially greater than for the overall population.