AI 中文总结
adabay是开源R包，可快速评估校准贝叶斯组序贯设计，复现相关工具结果且运行速度更快，支持多类终点及多种决策规则。
AI 中文摘要
贝叶斯组序贯设计（GSD）结合了频率派GSD的效率、临床可解释的概率陈述以及原则性的外部证据整合能力。然而，由于在设计阶段评估频率派操作特征的成本较高，其在确证性试验中的应用受到阻碍，该成本通常需要在蒙特卡洛试验模拟循环中嵌套马尔可夫链蒙特卡洛或其他近似后验推断。我们推出adabay，一个开源R包，实现了用于快速评估和校准贝叶斯GSD的半模拟框架。试验数据路径通过蒙特卡洛模拟生成，而各期中分析的后验分布和后验尾概率通过解析计算或低维确定性求积法得到。通过将用户指定的任意先验近似为共轭分量的有限混合，实现了灵活的先验指定，尾概率诊断会标记决策阈值处近似不足的情况。该包为连续、二分类、计数和时间-事件终点提供了统一的应用程序编程接口，支持在有约束或无约束机制下，采用包含一个或多个有效性和无效性标准的后验概率决策规则，还包含一种预计算策略，可将阈值校准和期中分析时间选择与模拟过程解耦。adabay在连续和二分类案例研究中复现了BATSS和adaptr的操作特征，在连续案例中复现了解析的gsbDesign值，在计数案例中复现了BATSS的结果，所有结果均在蒙特卡洛误差范围内，且在8核环境下，每次虚拟试验的运行速度比BATSS快约4至5个数量级，比adaptr快1至超过2个数量级。该包以MIT许可证分发。
英文摘要
Bayesian group sequential designs (GSDs) combine the efficiency of frequentist GSDs with clinically interpretable probability statements and principled external evidence incorporation. However, their uptake in confirmatory trials has been held back by the cost of evaluating frequentist operating characteristics at the design stage, which often nests Markov chain Monte Carlo or another approximate posterior inference within a Monte Carlo trial-simulation loop. We introduce adabay, an open-source R package implementing a semi-simulation framework for the rapid evaluation and calibration of Bayesian GSDs. Trial data paths are simulated by Monte Carlo, while per-look posteriors and posterior tail probabilities are computed analytically or by low-dimensional deterministic quadrature. Flexible prior specification is achieved by approximating any user-specified prior with a finite mixture of conjugate components, with tail-probability diagnostics that flag inadequate approximations at the decision thresholds. The package offers a unified application programming interface for continuous, binary, count and time-to-event endpoints, supports posterior-probability decision rules with one or more efficacy and futility criteria under binding or non-binding regimes, and includes a precomputation strategy that decouples threshold calibration and look-time selection from the simulation pass. adabay reproduces the operating characteristics of BATSS and adaptr in the continuous and binary case studies, the analytic gsbDesign values in the continuous case, and the BATSS results in the count case, all within Monte Carlo error, while running approximately four to five orders of magnitude faster than BATSS and one to over two orders of magnitude faster than adaptr per virtual trial on eight cores. The package is distributed under an MIT licence.
