Abstract
Multi-arm multi-stage trial designs can bring notable gains in efficiency to the drug development process. However, for normally distributed endpoints, the determination of a design typically depends on the assumption that the patient variance in response is known. In practice, this will not usually be the case. To allow for unknown variance, previous research explored the performance of t-test statistics, coupled with a quantile substitution procedure for modifying the stopping boundaries, at controlling the familywise error-rate to the nominal level. Here, we discuss an alternative method based on Monte Carlo simulation that allows the group size and stopping boundaries of a multi-arm multi-stage t-test to be optimised, according to some nominated optimality criteria. We consider several examples, provide R code for general implementation, and show that our designs confer a familywise error-rate and power close to the desired level. Consequently, this methodology will provide utility in future multi-arm multi-stage trials.
Highlights
With the cost of drug development increasing, study designs that can enhance the efficiency of clinical research are of great interest
Utilising test statistics that assume known variance will result in operating characteristics that differ from their nominal level if the true variance is not equal to the specified value
A Monte Carlo based procedure was proposed for two-armed group sequential trials [7]. We extend it to multi-arm multistage (MAMS) trials
Summary
With the cost of drug development increasing, study designs that can enhance the efficiency of clinical research are of great interest. This approach exploits the fact that data are accumulated over time: incorporating interim analyses at which the study may be stopped early, reducing the required sample size This methodology was extended to allow multiple treatments to be compared to a shared control [2]. A limitation of this methodology in the case of normally distributed outcome data is that designs are usually determined under the supposition of known patient variance in response. This will not be the case at the design stage. Utilising test statistics that assume known variance will result in operating characteristics that differ from their nominal level if the true variance is not equal to the specified value
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