Abstract
Response‐adaptive designs allow the randomization probabilities to change during the course of a trial based on cumulated response data so that a greater proportion of patients can be allocated to the better performing treatments. A major concern over the use of response‐adaptive designs in practice, particularly from a regulatory viewpoint, is controlling the type I error rate. In particular, we show that the naïve z‐test can have an inflated type I error rate even after applying a Bonferroni correction. Simulation studies have often been used to demonstrate error control but do not provide a guarantee. In this article, we present adaptive testing procedures for normally distributed outcomes that ensure strong familywise error control by iteratively applying the conditional invariance principle. Our approach can be used for fully sequential and block randomized trials and for a large class of adaptive randomization rules found in the literature. We show there is a high price to pay in terms of power to guarantee familywise error control for randomization schemes with extreme allocation probabilities. However, for proposed Bayesian adaptive randomization schemes in the literature, our adaptive tests maintain or increase the power of the trial compared to the z‐test. We illustrate our method using a three‐armed trial in primary hypercholesterolemia.
Highlights
Clinical trials typically randomize patients using a fixed randomization scheme, where the probabilities of assigning patients to the experimental treatments and control are pre-specified and constant
A common method is to use equal randomization to the different arms of the trial. Such randomization schemes can mean that a substantial proportion of the trial participants will continue to be allocated to treatments that are not the best available, even if interim data indicates that one treatment is likely to be superior
Many classes of response-adaptive randomization (RAR) schemes have been proposed for binary outcomes, and there is a growing interest in RAR for continuous responses
Summary
Clinical trials typically randomize patients using a fixed randomization scheme, where the probabilities of assigning patients to the experimental treatments and control are pre-specified and constant. A common method is to use equal randomization to the different arms of the trial Such randomization schemes can mean that a substantial proportion of the trial participants will continue to be allocated to treatments that are not the best available, even if interim data indicates that one treatment is likely to be superior. Responseadaptive trials address this concern by adaptively changing the randomization probabilities, so that a greater proportion of patients are allocated to the treatment arm which has a better performance based on the cumulated response data. 23 out of the 59 trials identified in a review of multi-arm trials by Wason et al (2014) had a continuous outcome
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