A comparison between two treatments in a clinical trial with an ethical allocation design

TL;DR: In this paper, the authors compared two treatments, say A and B, in the context of a clinical trial using the Mann-Whitney-Wilcoxon (MWW) statistic.

Abstract: The present article compares two treatments, say A and B, in the context of a clinical trial. Let X and Y be, respectively, the responses corresponding to A and B which have some continuous distributions. Here, the comparison is done through the parameter θ=P(X

TL;DR: A flexible method of extending a study based on conditional power, where the significance of the treatment difference at the planned end is used to determine the number of additional observations needed and the critical value necessary after accruing those additional observations.

TL;DR: In this article, the authors provided a statistical inference on comparative performances of two treatments in a clinical trial under a two-stage adaptive allocation design, where a fixed number (2m+n, say) of subjects are available for treatment by any of the two competing treatments, for a particular ailment.

Abstract: The present article provides a statistical inference on comparative performances of two treatments in a clinical trial under a two-stage adaptive allocation design Suppose a fixed number (2m+n, say) of subjects are available for treatment by any of the two competing treatments, say, A and B for a particular ailment As per the proposed allocation design, 2m incoming subjects are randomised equally between A and B at the first stage Then, at the second stage, the remaining n subjects are exclusively assigned to the treatment which has higher observed median response evaluated in the first stage Under such an ethical allocation design we decide on the better treatment through an asymptotically distribution-free test procedure The related asymptotic results are also studied

TL;DR: In this paper , a distribution-free test procedure for comparing the effectiveness of two competing treatments A and B, say, in a clinical trial, is provided, where the relative treatment effect is measured by the functional θ=P(X

Abstract: The present article provides a distribution-free test procedure for comparing the effectiveness of two competing treatments A and B, say, in a clinical trial. Here, the relative treatment effect is measured by the functional θ=P(X

TL;DR: In this paper, a generalization of the Behrens-Fisher problem without assuming that the underlying distribution functions are continuous is proposed, which is also applicable to count or ordered categorical data.

Abstract: AbtsrcatThe present paper considers a generalization of the Behrens-Fisher problem without assuming that the underlying distribution functions are continuous so that the proposed procedure is also applicable to count or ordered categorical data. The proposed test is based on a generalization of the Wilcoxon-Mann-Whitney statistic where the data correspond to sequentially observed responses from patients allocated at random to two treatments in two stages. At stage 1, patients are allocated completely at random between two treatments. At the end of stage 1, the accumulated responses corresponding to the treatments are compared. At stage 2, the patients are assigned to the treatments in such a way that the more promising treatment receives more patients. Simulation comparisons of the proposed procedure with some natural competitors show a considerably larger number of allocations to the better treatment and a smaller average sample number with a little loss in power.