Adaptive multiarm multistage clinical trials. (11th February 2020)

• Record Type: Journal Article
• Authors: Ghosh, Pranab; Liu, Lingyun; Mehta, Cyrus
• Is Part Of: Statistics in medicine. Volume 39:Number 8(2020)
• Page Start: 1084
• Page End: 1102
• Publication Date: 2020-02-11
• Subjects: MAMS -- early stopping -- P‐value combination -- Dunnett -- adaptive Dunnett -- two‐stage design -- multistage design -- sample size reestimation -- treatment selection -- FWER -- pairwise comparison -- closed testing -- cumulative MAMS -- adaptive MAMS -- seamless phase 2‐3; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.8464
• Languages: English
• ISSNs: 0277-6715

Abstract

Abstract : Two methods for designing adaptive multiarm multistage (MAMS) clinical trials, originating from conceptually different group sequential frameworks are presented, and their operating characteristics are compared. In both methods pairwise comparisons are made, stage‐by‐stage, between each treatment arm and a common control arm with the goal of identifying active treatments and dropping inactive ones. At any stage one may alter the future course of the trial through adaptive changes to the prespecified decision rules for treatment selection and sample size reestimation, and notwithstanding such changes, both methods guarantee strong control of the family‐wise error rate. The stage‐wise MAMS approach was historically the first to be developed and remains the standard method for designing inferentially seamless phase 2‐3 clinical trials. In this approach, at each stage, the data from each treatment comparison are summarized by a single multiplicity adjusted P ‐value. These stage‐wise P ‐values are combined by a prespecified combination function and the resultant test statistic is monitored with respect to the classical two‐arm group sequential efficacy boundaries. The cumulative MAMS approach is a more recent development in which a separate test statistic is constructed for each treatment comparison from the cumulative data at each stage. These statistics are then monitored with respect to multiplicity adjusted group sequential efficacy boundaries. We compared the powers of the two methods for designs with two and three active treatment arms, under commonly utilized decision rules for treatment selection, sample size reestimation and early stopping. In our investigations, which were carried out over a reasonably exhaustive exploration of the parameter space, the cumulative MAMS designs were more powerful than the stage‐wise MAMS designs, except for the homogeneous case of equal treatment effects, where a small power advantage was discernable for the stage‐wise MAMS designs.