Journal of biopharmaceutical statistics
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When conducting clinical trials with hierarchically ordered objectives, it is essential to use multiplicity adjustment methods that control the familywise error rate in the strong sense while taking into account the logical relations among the null hypotheses. This paper proposes a gatekeeping procedure based on the Hommel (1988) test, which offers power advantages compared to other p value-based tests proposed in the literature. A general description of the procedure is given and details are presented on how it can be applied to complex clinical trial designs. Two clinical trial examples are given to illustrate the methodology developed in the paper.
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In pharmaceutical drug development, for regulatory purposes, there are increasing discussions on the establishment of statistically significant results demonstrating the efficacy of a new treatment on multiple co-primary endpoints. At the design stage with multiple co-primary endpoints, it is critical to determine the appropriate sample size for indicating statistical significance for all co-primary endpoints with preserving the intended power set, since the type II error increases as the number of co-primary endpoints increases. We provide fundamental formulae for power and sample size calculation with multiple co-primary endpoints and illustrate the aspect of the provided methods through numerical tables and examples.
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The last 10 years have seen considerable interest in clinical trial designs that allow the seamless combination of Phases II and III in a single clinical trial. Such designs bring together the selection of the most promising of a number of treatments, as usually performed in a Phase II clinical trial, with the rigorous analysis and control of type I error rates required for a Phase III clinical trial. ⋯ This paper reviews methods based on the group-sequential methodology for monitoring of sequential clinical trials. The main focus of the paper will be a description of the methodology, including the setting in which short-term data are used for decision making at an early interim analysis.
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A clinical research program for drug development often consists of a sequence of clinical trials that may begin with uncontrolled and nonrandomized trials, followed by randomized trials or randomized controlled trials. Adaptive designs are not infrequently proposed for use. In the regulatory setting, the success of a drug development program can be defined to be that the experimental treatment at a specific dose level including regimen and frequency is approved based on replicated evidence from at least two confirmatory trials. ⋯ For confirmatory adaptive design clinical trials, controlling studywise type I error and type II error is of paramount importance. For exploratory adaptive trials, we define the probability of correct selection of design features, e.g., dose, effect size, and the probability of correct decision for drug development. We assert that maximizing these probabilities would be critical to determine whether the drug development program continues or how to plan the confirmatory trials if the development continues.