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      Some recommendations for multi-arm multi-stage trials

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          Abstract

          Multi-arm multi-stage designs can improve the efficiency of the drug-development process by evaluating multiple experimental arms against a common control within one trial. This reduces the number of patients required compared to a series of trials testing each experimental arm separately against control. By allowing for multiple stages experimental treatments can be eliminated early from the study if they are unlikely to be significantly better than control. Using the TAILoR trial as a motivating example, we explore a broad range of statistical issues related to multi-arm multi-stage trials including a comparison of different ways to power a multi-arm multi-stage trial; choosing the allocation ratio to the control group compared to other experimental arms; the consequences of adding additional experimental arms during a multi-arm multi-stage trial, and how one might control the type-I error rate when this is necessary; and modifying the stopping boundaries of a multi-arm multi-stage design to account for unknown variance in the treatment outcome. Multi-arm multi-stage trials represent a large financial investment, and so considering their design carefully is important to ensure efficiency and that they have a good chance of succeeding.

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          A multiple testing procedure for clinical trials.

          A multiple testing procedure is proposed for comparing two treatments when response to treatment is both dichotomous (i.e., success or failure) and immediate. The proposed test statistic for each test is the usual (Pearson) chi-square statistic based on all data collected to that point. The maximum number (N) of tests and the number (m1 + m2) of observations collected between successive tests is fixed in advance. The overall size of the procedure is shown to be controlled with virtually the same accuracy as the single sample chi-square test based on N(m1 + m2) observations. The power is also found to be virtually the same. However, by affording the opportunity to terminate early when one treatment performs markedly better than the other, the multiple testing procedure may eliminate the ethical dilemmas that often accompany clinical trials.
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            Adaptive designs for confirmatory clinical trials.

            Adaptive designs play an increasingly important role in clinical drug development. Such designs use accumulating data of an ongoing trial to decide how to modify design aspects without undermining the validity and integrity of the trial. Adaptive designs thus allow for a number of possible adaptations at midterm: Early stopping either for futility or success, sample size reassessment, change of population, etc. A particularly appealing application is the use of adaptive designs in combined phase II/III studies with treatment selection at interim. The expectation has arisen that carefully planned and conducted studies based on adaptive designs increase the efficiency of the drug development process by making better use of the observed data, thus leading to a higher information value per patient.In this paper we focus on adaptive designs for confirmatory clinical trials. We review the adaptive design methodology for a single null hypothesis and how to perform adaptive designs with multiple hypotheses using closed test procedures. We report the results of an extensive simulation study to evaluate the operational characteristics of the various methods. A case study and related numerical examples are used to illustrate the key results. In addition we provide a detailed discussion of current methods to calculate point estimates and confidence intervals for relevant parameters.
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              Sequential designs for phase III clinical trials incorporating treatment selection.

              Most statistical methodology for phase III clinical trials focuses on the comparison of a single experimental treatment with a control. An increasing desire to reduce the time before regulatory approval of a new drug is sought has led to development of two-stage or sequential designs for trials that combine the definitive analysis associated with phase III with the treatment selection element of a phase II study. In this paper we consider a trial in which the most promising of a number of experimental treatments is selected at the first interim analysis. This considerably reduces the computational load associated with the construction of stopping boundaries compared to the approach proposed by Follman, Proschan and Geller (Biometrics 1994; 50: 325-336). The computational requirement does not exceed that for the sequential comparison of a single experimental treatment with a control. Existing methods are extended in two ways. First, the use of the efficient score as a test statistic makes the analysis of binary, normal or failure-time data, as well as adjustment for covariates or stratification straightforward. Second, the question of trial power is also considered, enabling the determination of sample size required to give specified power. Copyright 2003 John Wiley & Sons, Ltd.
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                Author and article information

                Journal
                Stat Methods Med Res
                Stat Methods Med Res
                SMM
                spsmm
                Statistical Methods in Medical Research
                SAGE Publications (Sage UK: London, England )
                0962-2802
                1477-0334
                12 December 2012
                April 2016
                : 25
                : 2
                : 716-727
                Affiliations
                [1 ]MRC Biostatistics Unit, Cambridge, UK
                [2 ]Medical and Pharmaceutical Statistics Research Unit, Department of Mathematics and Statistics, Lancaster University, UK
                Author notes
                [*]James Wason, MRC Biostatistics Unit, Institute of Public Health, Robinson Way, Cambridge CB2 0SR, United Kingdom Email: james.wason@ 123456mrc-bsu.cam.ac.uk
                Article
                10.1177_0962280212465498
                10.1177/0962280212465498
                4843088
                23242385
                f9baa721-1d26-42a2-9fc9-0b3abdfff3bd
                © The Author(s) 2012

                This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 3.0 License ( http://www.creativecommons.org/licenses/by-nc/3.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page( https://us.sagepub.com/en-us/nam/open-access-at-sage).

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                clinical trial design,group-sequential designs,interim analysis,multi-arm multi-stage designs,multiple-testing,statistical design

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