Journal of biopharmaceutical statistics
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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.
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We consider analysis of two identical pivotal trials with correlated multiple hypotheses evaluated by the fixed-sequence, weighted Holm, or fallback procedure. For approval, at least one hypothesis must be rejected in both studies. Various weights are considered for the fallback and weighted Holm procedure to provide separation in a single study. ⋯ However, the fixed sequence often has the highest chance of obtaining inconsistent results between the two independent studies, which makes it less appealing. The weighted Holm and fallback procedures are very similar, with various weighting schemes providing modest differentiation. The alpha exhaustive version of the fallback procedure often has higher power for some endpoints and lower power for other endpoints compared to the weighted Holm procedure, but the differences are rarely large.