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 rank test is considered where the asymptotic variance is estimated consistently by using the ranks over all observations as well as the ranks within each sample and the consistency of the estimator is derived in the appendix.

Abstract: A generalization of the Behrens-Fisher problem for two samples is examined in a nonparametric model. It is not assumed that the underlying distribution functions are continuous so that data with arbitrary ties can be handled. A rank test is considered where the asymptotic variance is estimated consistently by using the ranks over all observations as well as the ranks within each sample. The consistency of the estimator is derived in the appendix. For small samples (n 1 , n 2 > 10), a simple approximation by a central t-distribution is suggested where the degrees of freedom are taken from the Satterthwaite-Smith-Welch approximation in the parametric Behrens-Fisher problem. It is demonstrated by means of a simulation study that the Wilcoxon-Mann-Whitney-test may be conservative or liberal depending on the ratio of the sample sizes and the variances of the underlying distribution functions. For the suggested approximation, however, it turns out that the nominal level is maintained rather accurately. The suggested nonparametric procedure is applied to a data set from a clinical trial. Moreover, a confidence interval for the nonparametric treatment effect is given.

TL;DR: In this paper, a flexible method of extending a study based on conditional power is proposed, 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 for use after accruing those additional observations.

Abstract: We propose a flexible method of extending a study based on conditional power. The possibility for extension when the p value at the planned end is small but not statistically significant is built in to the design of the study. 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 for use after accruing those additional observations. It may therefore be thought of as a two-stage procedure. Even though the observed treatment difference at stage 1 is used to make decisions, the Type I error rate is protected.

TL;DR: In this article, a simple randomized treatment assignment rule is proposed and analyzed in a sequential medical trial, and on the average this rule assigns more patients to the better treatment, and it is applicable to the case where patients have delayed responses to treatments.

Abstract: In a sequential medical trial, a simple randomized treatment assignment rule is proposed and analyzed. On the average this rule assigns more patients to the better treatment, and it is applicable to the case where patients have delayed responses to treatments. This new assignment rule is studied for both a fixed sample size and an inverse stopping rule.

TL;DR: Investigation of the conduct of a clinical trial where the “Play the Winner Rule” (PWR) is used to assign patients to the different therapies shows that over a wide range of situations this rule leads to near optimum results when used in a two-stage manner.

Abstract: Consider a clinical trial to compare two treatments where response is dichotomous and patients enter the trial sequentially. This paper investigates the conduct of such a trial where the “Play the Winner Rule” (PWR) is used to assign patients to the different therapies. The implementation of the PWR in a clinical trial tends to place more patients on the better treatment. Both theoretical and numerical investigations show that over a wide range of situations this rule leads to near optimum results when used in a two-stage manner. Furthermore, these results are insensitive to optimum sample size requirements.

TL;DR: Several commonly considered adaptive designs in clinical trials are reviewed and some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given.

Abstract: In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, adaptive designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective adaptive designs. An adaptive design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered adaptive designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of adaptive design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing adaptive design methods in clinical trials are also discussed.