论文标题
使用历史数据严格控制家庭错误率的贝叶斯多元成功概率
Bayesian Multivariate Probability of Success Using Historical Data with Strict Control of Family-wise Error Rate
论文作者
论文摘要
鉴于第三阶段和IV期临床试验的成本和持续时间，GO/NO-GO决策的统计方法的发展至关重要。在本文中，我们引入了一种贝叶斯方法，以根据多元线性模型的治疗方案的当前数据计算成功的可能性。我们的方法利用贝叶斯看似无关的回归模型，即使端点之间的协变量不同，该模型即使可以共同建模多个终点。端点之间的相关性是明确建模的。这种贝叶斯联合建模方法在一个框架下统一了单个和多个测试程序。我们开发了一种多次测试方法，该方法渐近地保证了严格的家庭错误率控制，并且比频繁的多样性方法更强大。该方法有效地产生了Ibrahim等人的方法。据我们所知，Chuang-Stein是特殊情况，并且是唯一允许对多个终点和/或假设确定样本大小的方法，也是唯一在存在多重性的情况下提供严格的家庭型I错误控制的方法。
Given the cost and duration of phase III and phase IV clinical trials, the development of statistical methods for go/no-go decisions is vital. In this paper, we introduce a Bayesian methodology to compute the probability of success based on the current data of a treatment regimen for the multivariate linear model. Our approach utilizes a Bayesian seemingly unrelated regression model, which allows for multiple endpoints to be modeled jointly even if the covariates between the endpoints are different. Correlations between endpoints are explicitly modeled. This Bayesian joint modeling approach unifies single and multiple testing procedures under a single framework. We develop an approach to multiple testing that asymptotically guarantees strict family-wise error rate control, and is more powerful than frequentist approaches to multiplicity. The method effectively yields those of Ibrahim et al. and Chuang-Stein as special cases, and, to our knowledge, is the only method that allows for robust sample size determination for multiple endpoints and/or hypotheses and the only method that provides strict family-wise type I error control in the presence of multiplicity.
