Power and sample size analysis comprises a critical component of clinical trial study design. There is an extensive collection of methods addressing this problem from diverse perspectives. The Bayesian paradigm, in particular, has attracted noticeable attention and includes different perspectives for sample size determination. Building upon a cost-effectiveness analysis undertaken by O'Hagan and Stevens (2001) with different priors in the design and analysis stage, we develop a general Bayesian framework for simulation-based sample size determination that can be easily implemented on modest computing architectures. We further qualify the need for different priors for the design and analysis stage. We work primarily in the context of conjugate Bayesian linear regression models, where we consider the situation with known and unknown variances. Throughout, we draw parallels with frequentist solutions, which arise as special cases, and alternate Bayesian approaches with an emphasis on how the numerical results from existing methods arise as special cases in our framework.
翻译:电力和抽样规模分析包括临床试验研究设计的关键组成部分。从不同角度广泛收集了解决这一问题的方法。贝耶斯模式尤其吸引了显著的关注,并包括了确定抽样规模的不同观点。根据奥哈根和史蒂文斯(2001年)进行的成本效益分析,在设计和分析阶段具有不同前科,我们制定了一个通用的巴耶斯框架,用于模拟抽样规模的确定,可以在适度的计算结构中轻易实施。我们进一步限定了设计和分析阶段需要不同的前科。我们主要在共同的巴伊西亚线性回归模型中工作,我们在这里考虑已知和未知差异的情况。我们从总体上与常见的解决方案(作为特例出现)和替代的巴伊斯方法平行,重点是如何将现有方法的数值结果作为我们框架中的特殊案例产生。