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: A randomized two-stage adaptive Bayesian design is proposed and studied for allocation and comparison in a phase III clinical trial with survival time as treatment response and the applicability of the proposed methodology is illustrated.

Abstract: A randomized two-stage adaptive Bayesian design is proposed and studied for allocation and comparison in a phase III clinical trial with survival time as treatment response. Several exact and limiting properties of the design and the follow-up inference are studied, both numerically and theoretically, and are compared with a single-stage randomized procedure. The applicability of the proposed methodology is illustrated by using some real data.

TL;DR: In this article, a two-treatment allocation scheme based on Wilcoxon-Mann-Whitney scores was proposed to provide two nonparametric test procedures for identity of univariate continuous populations against location alternatives.

TL;DR: A conditional likelihood-based approach is proposed to construct confidence intervals for the parameters of interest in a two-stage design with treatment selection after the first stage using a Wald confidence interval and a confidence interval based on inverting the likelihood ratio test.

Abstract: A conditional likelihood-based approach is proposed to construct confidence intervals for the parameters of interest in a two-stage design with treatment selection after the first stage. Both a Wald confidence interval and a confidence interval based on inverting the likelihood ratio test are proposed. The operating characteristics of these confidence intervals: the coverage probabilities and average confidence interval lengths, as well as the average bias and mean-square error of the corresponding point estimates, compare favorably with other available techniques. Possible extensions and an alternative unconditional approach based on the likelihood with missing at random mechanism are also briefly described.

TL;DR: The connection between permutation and t-tests is shown, and this connection is used to explain why certain adaptations are valid in a t-test setting as well.

Abstract: A permutation test assigns a p-value by conditioning on the data and treating the different possible treatment assignments as random. The fact that the conditional type I error rate given the data is controlled at level α ensures validity of the test even if certain adaptations are made. We show the connection between permutation and t-tests, and use this connection to explain why certain adaptations are valid in a t-test setting as well. We illustrate this with an example of blinded sample size recalculation.