Chen Dung-Tsa, Schell Michael J, Fulp William J, Pettersson Fredrik, Kim Sungjune, Gray Jhanelle E, Haura Eric B
Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
Department of Radiation Oncology, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA.
Transl Cancer Res. 2019 Jul;8(Suppl 4):S404-S420. doi: 10.21037/tcr.2019.05.17.
Bayesian predictive probability design, with a binary endpoint, is gaining attention for the phase II trial due to its innovative strategy. To make the Bayesian design more accessible, we elucidate this Bayesian approach with a R package to streamline a statistical plan, so biostatisticians and clinicians can easily integrate the design into clinical trial.
We utilize a Bayesian framework using Bayesian posterior probability and predictive probability to build a R package and develop a statistical plan for the trial design. With pre-defined sample sizes, the approach employs the posterior probability with a threshold to calculate the minimum number of responders needed at end of the study to claim efficacy. Then the predictive probability is applied to evaluate future success at interim stages and form stopping rule at each stage.
An R package, 'BayesianPredictiveFutility', with associated graphical interface is developed for easy utilization of the trial design. The statistical tool generates a professional statistical plan with comprehensive results including a summary, details of study design, a series of tables and figures from stopping boundary for futility, Bayesian predictive probability, performance [probability of early termination (PET), type I error, and power], PET at each interim analysis, sensitivity analysis for predictive probability, posterior probability, sample size, and beta prior distribution. The statistical plan presents the methodology in a readable language fashion while preserving rigorous statistical arguments. The output formats (Word or PDF) are available to communicate with physicians or to be incorporated in the trial protocol. Two clinical trials in lung cancer are used to demonstrate its usefulness.
Bayesian predictive probability method presents a flexible design in clinical trial. The statistical tool brings an added value to broaden the application.
贝叶斯预测概率设计因创新策略在II期试验中受到关注，其终点为二元变量。为使贝叶斯设计更易于应用，我们用一个R包阐释这种贝叶斯方法，以简化统计计划，从而让生物统计学家和临床医生能够轻松地将该设计整合到临床试验中。
我们利用贝叶斯框架，借助贝叶斯后验概率和预测概率构建一个R包，并为试验设计制定统计计划。该方法在预先确定样本量的情况下，使用带有阈值的后验概率来计算研究结束时宣称疗效所需的最小反应者数量。然后应用预测概率来评估中期阶段未来成功的可能性，并在每个阶段形成停止规则。
开发了一个带有相关图形界面的R包“BayesianPredictiveFutility”，便于使用试验设计。该统计工具生成一个专业的统计计划，结果全面，包括总结、研究设计细节、一系列来自无效性停止边界、贝叶斯预测概率、性能（早期终止概率、I类错误和检验效能）、每次中期分析时的早期终止概率、预测概率的敏感性分析、后验概率、样本量和贝塔先验分布的表格和图形。该统计计划以易读的语言形式呈现方法，同时保留严格的统计学论证。输出格式（Word或PDF）可用于与医生沟通或纳入试验方案。两项肺癌临床试验被用于证明其有用性。
贝叶斯预测概率方法在临床试验中呈现出灵活的设计。该统计工具为扩大应用带来了附加价值。
