Zhu Lin, Yu Qingzhao, Mercante Donald E
School of Public Health, Louisiana State University Health Sciences Center.
Stat Biopharm Res. 2019;11(4):387-397. doi: 10.1080/19466315.2019.1629996. Epub 2019 Jul 22.
There is increasing interest in Bayesian group sequential design because of its potential to improve efficiency in clinical trials, to shorten drug development time, and to enhance statistical inference precision without undermining the clinical trial's integrity or validity. We propose a Bayesian sequential design for clinical trials with time-to-event outcomes and use alpha spending functions to control the overall type I error rate. Bayes factor is adapted for decision-making at interim analyses. Algorithms are presented to make decision rules and to calculate power of the proposed tests. Sensitivity analysis is implemented to evaluate the impact of different choices of prior parameters on choosing critical values. The power of tests, the expected event size of the proposed design, and the quality of estimators are studied through simulations, and compared with the frequentist group sequential design. Simulations show that at fixed total number of events, the proposed design can achieve greater power and require smaller expected event size when appropriate priors are chosen, compared with the frequentist group sequential design. The feasibility of the proposed design is further illustrated on a real data set.
由于贝叶斯组序贯设计在提高临床试验效率、缩短药物研发时间以及在不损害临床试验完整性或有效性的前提下提高统计推断精度方面具有潜力，因此人们对其兴趣与日俱增。我们提出了一种针对具有事件发生时间结局的临床试验的贝叶斯序贯设计，并使用α消耗函数来控制总体I型错误率。在期中分析时采用贝叶斯因子进行决策。给出了用于制定决策规则和计算所提检验效能的算法。进行敏感性分析以评估先验参数的不同选择对选择临界值的影响。通过模拟研究了检验效能、所提设计的预期事件规模以及估计量的质量，并与频率学派组序贯设计进行了比较。模拟结果表明，在固定的事件总数下，与频率学派组序贯设计相比，当选择合适的先验时，所提设计能够获得更高的效能且所需的预期事件规模更小。在一个真实数据集上进一步说明了所提设计的可行性。
