论文标题
在具有多个终点的确认临床试验中，深层历史借款框架与前瞻性，同时合成控制信息
Deep Historical Borrowing Framework to Prospectively and Simultaneously Synthesize Control Information in Confirmatory Clinical Trials with Multiple Endpoints
论文作者
论文摘要
在当前的临床试验开发中，历史信息引起了更多关注，因为它提供了超出样本量计算的实用性。已经提出了荟萃分析预测性（地图）先验和强大的地图先验，以便在单个终点上借用历史数据。为了同时从确认性临床试验中的多个终点合成控制信息，我们建议通过深度学习来构建假设检验的预先指定的策略来估算贝叶斯分层模型的后验概率，并估算关键值。此功能对于通过在审判行为之前建立前瞻性决策职能来确保研究完整性很重要。进行仿真以表明我们的方法正确控制家庭错误率（FWER），并保留功率与在无效空间子集的情况下选择恒定临界值的典型实践相比。还证明了在先前数据冲突下的令人满意的表现。我们使用免疫学案例研究进一步说明了我们的方法。
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.
