Journal of biopharmaceutical statistics
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The van Elteren test, as a type of stratified Wilcoxon-Mann-Whitney test for comparing two treatments accounting for stratum effects, has been used to replace the analysis of variance when the normality assumption was seriously violated. The sample size estimation methods for the van Elteren test have been proposed and evaluated previously. ⋯ Theories and simulations have shown that the new method performs well when the location-scale assumption holds and works reasonably when the assumption does not hold. Thus, the new method is preferred when computing sample sizes for the van Elteren test in active-comparator trials.
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Due to ethical and practical issues, clinical trials are conducted in multiple stages, but the reported p-values often fail to reflect the design aspect of the trials. We investigate some approaches to p-value calculation in analyzing multi-stage Phase II clinical trials that have a binary variable, such as response, as the primary endpoint. ⋯ We consider the orderings based on the maximum likelihood estimator and the uniformly minimum variance unbiased estimator. We will compare, using some examples, the p-values based on these alternative orderings and the one ignoring the multistage design aspect of phase II trials.
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Comparative Study
Adaptive statistical analysis following sample size modification based on interim review of effect size.
In designing a comparative clinical trial, the required sample size is a function of the effect size, the value of which is unknown and at best may be estimated from historical data. Insufficiency in sample size as a result of overestimating the effect size can be destructive to the success of the clinical trial. ⋯ This paper is intended to give the motivations for the sample size re-estimation based partly on the effect size observed at an interim analysis and for a resulting simple adaptive test strategy. The performance of this adaptive design strategy is assessed by comparing it with a fixed maximum sample size design that is properly adjusted in anticipation of the possible sample size adjustment.
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In recent years, the use of adaptive design methods based on accrued data of on-going trials have become very popular for dose response trials in early clinical development due to their flexibility (EMEA, 2002). In this paper, we developed a hybrid frequentist-Bayesian continual reassessment method (CRM) in conjunction with utility-adaptive randomization for clinical trial designs with multiple endpoints. ⋯ The proposed utility-adaptive randomization for multiple-endpoint trials allows more patients to be assigned to superior treatment groups. The performance of the proposed method was examined in terms of its operating characteristics through computer simulations.
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Comparative Study
A Bayesian approach on sample size calculation for comparing means.
In clinical research, parameters required for sample size calculation are usually unknown. A typical approach is to use estimates from some pilot studies as the true parameters in the calculation. This approach, however, does not take into consideration sampling error. ⋯ Then, the traditional sample size calculation procedure can be carried out using the Bayesian estimates instead of the frequentist estimates. The results indicate that the sample size obtained using the Bayesian approach differs from the traditional sample size obtained by a constant inflation factor, which is purely determined by the size of the pilot study. An example is given for illustration purposes.