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 article, the authors show that the usual Wilcoxon test, with $(m + n + 1)/12mn$ replaced by $1/\lbrack 4 \min(m, n)brack$, still works for testing the equality of the medians of any two symmetrical distributions which are of the same form but which have different scale parameters.

Abstract: Heretofore, the ordinary Wilcoxon statistic for the two-sample problem [9], [5] has been used only to test the null hypothesis that the two parent populations are identical. This paper presents a technique for utilizing the Wilcoxon statistic to test a broader type of null hypothesis, like that encountered in the Behrens-Fisher problem: we show that the usual Wilcoxon test, with $(m + n + 1)/12mn$ replaced by $1/\lbrack 4 \min(m, n)\rbrack$, may be used to test the null hypothesis of the equality of the medians of two symmetrical (continuous) distributions which are of the same form but which have different (unknown) scale parameters; more generally, the test still works for testing the equality of the medians of any two symmetrical distributions.

TL;DR: The bivariate probit dose-response model is adopted and the two-stage designs are suggested to be as efficient as and may be more efficient than the fully adaptive designs if there is a moderate sample size in the initial stage.

Abstract: In dose-finding clinical studies, it is common that multiple endpoints are of interest. For instance, in phase I/II studies, efficacy and toxicity are often the primary endpoints, which are observed simultaneously and which need to be evaluated together. Motivated by this, we confine ourselves to bivariate responses and focus on the most analytically difficult case: a mixture of continuous and categorical responses. We adopt the bivariate probit dose–response model and quantify our goal by a utility function. We study locally optimal designs, two-stage optimal designs, and fully adaptive designs under different ethical and cost constraints in the experiments. We assess the performance of two-stage designs and fully adaptive designs via simulations. Our simulations suggest that the two-stage designs are as efficient as and may be more efficient than the fully adaptive designs if there is a moderate sample size in the initial stage. In addition, two-stage designs are easier to construct and implement and thus can be a useful approach in practice. Copyright © 2011 John Wiley & Sons, Ltd.

TL;DR: In this article, a technique for utilizing the Wilcoxon statistic to test a broader type of null hypothesis, like that encountered in the Behrens-Fisher problem, was presented, where the usual Wilcoxton test, with (m + n + 1)/12mn replaced by 1/[4 min(m, n)], may be used to test the null hypothesis of the equality of the medians of two symmetrical (continuous) distributions which are of the same form but which have different scale parameters.

Abstract: 0. Summary. Heretofore, the ordinary Wilcoxon statistic for the two-sample problem [9], [5] has been used only to test the null hypothesis that the two parent populations are identical. This paper presents a technique for utilizing the Wilcoxon statistic to test a broader type of null hypothesis, like that encountered in the Behrens-Fisher problem: we show that the usual Wilcoxon test, with (m + n + 1)/12mn replaced by 1/[4 min(m, n)], may be used to test the null hypothesis of the equality of the medians of two symmetrical (continuous) distributions which are of the same form but which have different (unknown) scale parameters; more generally, the test still works for testing the equality of the medians of any two symmetrical distributions. 1. Introduction and statement of results. We have a sample X m Xi's and a sample of n Yj's from parent populations with c.d.f.'s G(x) anld H(y) respectively. It is known that

TL;DR: The results show that the proposed optimal designs are frequently unbalanced and that they are generally more efficient and more ethical than the popular balanced designs.

Abstract: We considered design issues for multiple treatment arms in survival intervention trials and used optimal design theory to allocate patients adaptively in such trials. We proposed three types of optimal designs: one ensures that we have the most precise estimates of the treatment effects, another guarantees that we have the minimal sample size subject to user-specified allocation ratio assignments among treatment arms, and the third ensures that the design has minimal total hazard for the cohort. The latter two types of optimal designs are also subject to user-specified power constraints for testing contrasts among treatment effects. The operating characteristics of these optimal designs along with balanced designs are compared theoretically and by simulation, including their robustness properties with respect to model misspecifications. Our results show that the proposed optimal designs are frequently unbalanced and that they are generally more efficient and more ethical than the popular balanced designs. We also apply our response-adaptive allocation strategy to redesign a three-arm head and neck cancer trial and make comparisons.