Investigators who manage multicenter clinical trials need to pay careful attention to patterns of subject accrual, and the prediction of activation time for pending centers is potentially crucial for subject accrual prediction. We propose a Bayesian hierarchical model to predict subject accrual for multicenter clinical trials in which center activation times vary. We define center activation time as the time at which a center can begin enrolling patients in the trial. The difference in activation times between centers is assumed to follow an exponential distribution, and the model of subject accrual integrates prior information for the study with actual enrollment progress. We apply our proposed Bayesian multicenter accrual model to two multicenter clinical studies. The first is the PAIN‐CONTRoLS study, a multicenter clinical trial with a goal of activating 40 centers and enrolling 400 patients within 104 weeks. The second is the HOBIT trial, a multicenter clinical trial with a goal of activating 14 centers and enrolling 200 subjects within 36 months. In summary, the Bayesian multicenter accrual model provides a prediction of subject accrual while accounting for both center‐ and individual patient‐level variation.

中文翻译：

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具有不同中心激活时间的多中心临床试验中的贝叶斯应计建模和预测。

管理多中心临床试验的研究人员需要密切关注受试者增加的模式，待定中心激活时间的预测对于受试者增加预测可能至关重要。我们提出了一个贝叶斯分层模型来预测多中心临床试验的受试者累积，其中中心激活时间不同。我们将中心激活时间定义为中心可以开始招募患者参加试验的时间。假设中心之间激活时间的差异遵循指数分布，并且受试者应计模型将研究的先验信息与实际注册进度相结合。我们将我们提出的贝叶斯多中心应计模型应用于两项多中心临床研究。第一个是疼痛控制研究，一项多中心临床试验，目标是在 104 周内激活 40 个中心并招募 400 名患者。第二个是 HOBIT 试验，这是一项多中心临床试验，目标是在 36 个月内激活 14 个中心并招募 200 名受试者。总之，贝叶斯多中心应计模型提供了对受试者应计的预测，同时考虑了中心和个体患者水平的变化。