In current clinical trial development, historical information is receiving more attention as it provides utility beyond sample size calculation. Meta-analytic-predictive (MAP) priors and robust MAP priors have been proposed for prospectively borrowing historical data on a single endpoint. To simultaneously synthesize control information from multiple endpoints in confirmatory clinical trials, we propose to approximate posterior probabilities from a Bayesian hierarchical model and estimate critical values by deep learning to construct pre-specified strategies for hypothesis testing. This feature is important to ensure study integrity by establishing prospective decision functions before the trial conduct. Simulations are performed to show that our method properly controls family-wise error rate (FWER) and preserves power as compared with a typical practice of choosing constant critical values given a subset of null space. Satisfactory performance under prior-data conflict is also demonstrated. We further illustrate our method using a case study in Immunology.
翻译:在目前的临床试验开发中,历史信息正受到更多的注意,因为它提供了超出抽样规模计算范围的实用性。为了在单一端点上可能借用历史数据,提出了元分析预测前期和稳健的MAP前期建议。为了同时综合来自确认临床试验中多个端点的控制信息,我们提议从巴伊西亚等级模型中估计后端概率,并通过深思熟虑来建立预先指定的假设测试战略来估计关键值。这一特征很重要,通过在试验行为之前确定未来的决策功能来确保研究的完整性。进行模拟是为了表明我们的方法适当控制家庭错差率,并保持能力,与选择固定关键值的典型做法相比,因为有一组空隙空间。还演示了先前数据冲突下的满意性表现。我们进一步用Immunolog学案例研究来说明我们的方法。