A two-stage Bayesian design with sample size reestimation and subgroup analysis for phase II binary response trials.
A two-stage Bayesian design with sample size reestimation and subgroup analysis for phase II binary response trials.
复制标题
两阶段贝叶斯设计，用于 II 期二元反应试验的样本量重新估计和亚组分析。
DOI：
10.1016/j.cct.2013.03.011
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发表时间：
2013
影响因子：
2.2
通讯作者：
Carlin,BradleyP
中科院分区：
文献类型：
--
作者：
Zhong,Wei;Koopmeiners,JosephS;Carlin,BradleyP
双组临床试验中二元结果数据的频率论样本量确定需要对两种治疗的事件概率进行初步猜测。这些事件发生率的错误指定可能会导致对必要样本量的估计不佳。相比之下，将治疗效果视为具有某种分布的随机变量的贝叶斯方法可能提供更好、更灵活的方法。 Whitehead 等人，2008 年 [27]）提出的用于功效探索性研究的贝叶斯样本量通过“结论性”条件证明了可接受的最小样本量的合理性。在这项工作中，我们引入了一种新的两阶段贝叶斯设计，在中期阶段重新估计样本量。我们的设计继承了 Whitehead 等人的良好解释和易于实施的特性。 (2008) [27]，将他们的方法推广到两个样本设置，并在必要时使用完全贝叶斯预测方法来减少过大的初始样本量。此外，我们的设计可以扩展为通过逻辑回归允许患者水平协变量，现在根据中期分析调整每个亚组内的样本量。我们通过使用简单的二元协变量（患者性别）的非霍奇金淋巴瘤设计来说明我们方法的好处，为试验内个性化医疗迈出了第一步。
Frequentist sample size determination for binary outcome data in a two-arm clinical trial requires initial guesses of the event probabilities for the two treatments. Misspecification of these event rates may lead to a poor estimate of the necessary sample size. In contrast, the Bayesian approach that considers the treatment effect to be random variable having some distribution may offer a better, more flexible approach. The Bayesian sample size proposed by (Whitehead et al., 2008 [27]) for exploratory studies on efficacy justifies the acceptable minimum sample size by a “conclusiveness” condition. In this work, we introduce a new two-stage Bayesian design with sample size reestimation at the interim stage. Our design inherits the properties of good interpretation and easy implementation from Whitehead et al. (2008) [27], generalizes their method to a two-sample setting, and uses a fully Bayesian predictive approach to reduce an overly large initial sample size when necessary. Moreover, our design can be extended to allow patient level covariates via logistic regression, now adjusting sample size within each subgroup based on interim analyses. We illustrate the benefits of our approach with a design in non-Hodgkin lymphoma with a simple binary covariate (patient gender), offering an initial step toward within-trial personalized medicine.