An optimised multi-arm multi-stage clinical trial design for unknown variance.

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

SUBMITTER: Grayling MJ 

PROVIDER: S-EPMC5886309 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature