bayprior：结构化贝叶斯先验引出、冲突诊断与监管报告
bayprior: Structured Bayesian Prior Elicitation, Conflict Diagnostics, and Regulatory Reporting
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中文总结 AI 辅助
bayprior是一个R包和Shiny应用，整合贝叶斯先验引出、冲突诊断、敏感性分析和监管报告，支持FDA和EMA指南，并通过合成肿瘤学试验验证。
中文摘要 AI 辅助
贝叶斯临床试验需要明确定义的先验分布，监管机构日益期望该定义被记录、针对先验-数据冲突进行诊断，并证明其对合理替代方案具有稳健性——然而，很少有R工具能将先验构建、验证和监管论证的完整工作流程整合到一个包中。我们推出bayprior，一个R包和Shiny应用程序，它通过分位数匹配、矩匹配和SHELF轮盘法在六个分布族中实现结构化专家引出；通过Bhattacharyya一致性诊断实现线性和对数专家意见汇总；使用Box p值、惊奇指数、信息散度、Bhattacharyya重叠和多元马氏距离评估先验-数据冲突；通过龙卷风和影响热图可视化进行超参数敏感性分析；并提供稳健、怀疑和功率先验替代方案。一个监管报告模块生成自包含的HTML、PDF或Word文档，以回应FDA 2026年贝叶斯方法指南草案和EMA关于纳入外部和历史信息的指南中的期望。该包在一个合成的肿瘤学II期试验上进行了演示。bayprior可在CRAN上获得，并包含一个完全模块化的Shiny应用程序供交互使用。
英文摘要
Bayesian clinical trials require a well-specified prior distribution, and regulators increasingly expect that specification to be documented, diagnosed for prior-data conflict, and shown to be robust to reasonable alternatives -- yet few R tools connect the full workflow of prior construction, validation, and regulatory justification into a single package. We introduce bayprior, an R package and Shiny application that implements structured expert elicitation via quantile matching, moment matching, and the SHELF roulette method across six distribution families; linear and logarithmic expert opinion pooling with Bhattacharyya agreement diagnostics; prior-data conflict assessment using the Box p-value, surprise index, information divergence, Bhattacharyya overlap, and multivariate Mahalanobis distance; hyperparameter sensitivity analysis with tornado and influence heatmap visualisations; and robust, sceptical, and power prior alternatives. A regulatory reporting module generates self-contained HTML, PDF, or Word documents addressing expectations in the FDA's 2026 draft Bayesian methods guidance and in EMA guidance on incorporating external and historical information. The package is demonstrated on a synthetic oncology Phase II trial. bayprior is available on CRAN and includes a fully modular Shiny application for interactive use.
