Response-adaptive treatment randomization for multiple comparisons of treatments with recurrent event responses

Abstract

Recurrent event responses are frequently encountered during clinical trials of treatments for certain diseases, such as asthma. The recurrence rates of different treatments are often compared by applying the negative binomial model. In addition, a balanced treatment-allocation procedure that assigns the same number of patients to each treatment is often applied. Recently, a response-adaptive treatment-allocation procedure has been developed for trials with recurrent event data, and has been shown to be superior to balanced treatment allocation. However, this response-adaptive treatment allocation procedure is only applicable for the comparison of two treatments. In this paper, we derive response-adaptive treatment-allocation procedures for trials which comprise several treatments. As pairwise comparisons and multiple comparisons with a control are two common multiple-testing scenarios in trials with more than two treatments, corresponding treatment-allocation procedures for these scenarios are also investigated. The redesign of two clinical studies illustrates the clinical benefits that would be obtained from our proposed response-adaptive treatment-allocation procedures.

An Improved Dunnett’s Procedure for Comparing Multiple Treatments with a Control in the Presence of Missing Observations

“…Gao developed a response-adaptive treatment-allocation procedure to compare efficacy between two treatments in trials with recurrent event data, which was shown to be superior to balanced treatment allocation [16]. Further, the procedure was improved for both pairwise comparisons and many-to-one comparisons [17]. As mentioned previously, many-to-one comparisons are often considered in dose response studies, especially in the early drug development process, where different dosage levels need to be compared with a control simultaneously.…”