论文标题
使用保证对两臂聚类随机试验的贝叶斯设计和分析
Bayesian design and analysis of two-arm cluster randomised trials using assurance
论文作者
论文摘要
我们考虑了具有连续结果的两臂优势随机对照试验（RCT）的设计。我们使用线性混合效应模型详细介绍了贝叶斯推断，以分析试验。将治疗与使用后验分布对照进行对照进行比较。我们开发了根据此分析选择样本量的保证形式，并使用两个环蒙特卡洛采样方案进行评估。我们评估了所提出的方法，考虑到不同形式的先前分布的影响，以及两个循环中所需的蒙特卡洛样品数量，以准确确定保证和样本量。基于此评估，我们就这些选择中的每一个都提供一般建议。我们将方法应用于群集RCT的样本量的方法中，并将所得的样本量与基于WALD测试的功率计算和保证的样本量进行比较。本文开发的贝叶斯设计和分析方法可以从所选样本大小到参数错误指定的鲁棒性增加以及样本量减少的情况下提供优势，如果先验信息表明治疗效果可能大于最小临床上重要的差异。
We consider the design of a two-arm superiority cluster randomised controlled trial (RCT) with a continuous outcome. We detail Bayesian inference for the analysis of the trial using a linear mixed-effects model. The treatment is compared to control using the posterior distribution for the treatment effect. We develop the form of the assurance to choose the sample size based on this analysis, and its evaluation using a two loop Monte Carlo sampling scheme. We assess the proposed approach, considering the effect of different forms of prior distribution, and the number of Monte Carlo samples needed in both loops for accurate determination of the assurance and sample size. Based on this assessment, we provide general advice on each of these choices. We apply the approach to the choice of sample size for a cluster RCT into post-stroke incontinence, and compare the resulting sample size to those from a power calculation and assurance based on a Wald test for the treatment effect. The Bayesian approach to design and analysis developed in this paper can offer advantages in terms of an increase in the robustness of the chosen sample size to parameter mis-specification and reduced sample sizes if prior information indicates the treatment effect is likely to be larger than the minimal clinically important difference.
