Journal of biopharmaceutical statistics
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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.
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Currently, methods for evaluation of equivalence under a matched-pair design use either difference in proportions or relative risk as measures of risk association. However, these measures of association are only for cross-sectional studies or prospective investigations, such as clinical trials and they cannot be applied to retrospective research such as case-control studies. As a result, under a matched-pair design, we propose the use of the conditional odds ratio for assessment of equivalence in both prospective and retrospective research. ⋯ A simulation study was conducted to empirically investigate the size and power of the proposed procedures. Simulation results show that the score test not only adequately controls the Type I error but it can also provide sufficient power. A numerical example illustrates the proposed methods.