Abstract
A class of adaptive designs is formulated in two stages for clinical trials to favour the better performing treatment for further allocation in an efficient way. The first stage of the allocation consists in randomizing subjects to each treatment arm with equal probability and performing a test of equality of treatment effects. The resulting p value and the available estimate of a treatment difference measure is used to assign the incoming second stage subjects. Considering binary and normal responses, several exact and asymptotic properties of the proposed allocation are thoroughly examined and compared with the existing allocation designs.
Highlights
The primary objective of any clinical trial is to determine the efficacy of the competing treatments on the basis of the responses of the participants of the trial
Perhaps the earliest, instance of two stage allocation in the field of a clinical trial can be found in Colton (1963), where for the assignment of n recruited subjects, 2m subjects, m on each treatment arm, are used in the first stage and the treatment doing better is selected for the assignment of the remaining (n − 2m) subjects
The present work develops an adaptive allocation procedure in two stages, where apart from being a function of the p value of a test of equality of treatment effects based on the first stage data, the second stage randomization probabilities are continuously updated after each response of the second stage assignments
Summary
The primary objective of any clinical trial is to determine the efficacy of the competing treatments on the basis of the responses of the participants of the trial. In a recent work, Bhattacharya and Shome (2015) developed a two stage allocation procedure using the sufficient statistics based on the first stage data in a convenient way To be specific, they used a decreasing function of the p value of a test of equality of treatment effects based on the first stage data to set the allocation probability of each subject of the second stage. The present work develops an adaptive allocation procedure in two stages, where apart from being a function of the p value of a test of equality of treatment effects based on the first stage data, the second stage randomization probabilities are continuously updated after each response of the second stage assignments.
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