Approximating the Operating Characteristics of Bayesian Uncertainty Directed Trial Designs
贝叶斯不确定性导向试验设计运行特征的近似AI 解读 ↓
← 返回论文列表与筛选
阅读说明
本页整理公开书目信息，不生成学术质量评分。AI 解读根据题名及所注明的摘要或原文片段生成，未完成全文核验，可能有误；具体假设、证明与结论请核对原文。摘要保留来源文本，未经模型改写。
DOI 首次登记不等于接收或发表日期。部分更正、评论等条目没有独立摘要；缺少可靠材料时保留待补状态。
本页为预印本版本；arXiv 收录、作者填写的期刊信息以及作者既有发表经历，都不单独证明本篇已接收。作者发表记录按 OpenAlex 身份与完整姓名、本站覆盖期刊 ISSN 及正式卷期核对，只展示已核实例，档案仍可能误合并。
引用量采用 OpenAlex 单条关联记录，不合计多个记录；关联记录可能包含正式发表版本，不等于此版本单独引用。缺失不视为零，引用指标不代表论文质量。
关注作者按书目中的完整姓名匹配，同名和姓名变体需人工辨别。相关论文先按共享细分方向和具体方法排列，再参考通用方法与发表日期。
分类依据与纠错
AI 根据所提供的论文材料归类：摘要研究以治疗效应信息为目标的临床试验自适应随机分配设计。
反馈分类问题
正在核对关联版本…
代码、数据与补充材料
正在核对对应版本的资源…
贝叶斯不确定性导向试验设计运行特征的近似
- 研究问题
- 贝叶斯响应自适应临床试验通常依赖耗时模拟评估功效等运行特征；能否通过大样本近似描述分配过程并减轻设计评估负担？
- 方法与思路
- 作者分析以信息不确定性指标驱动分配的 BUD 设计，推导患者分配数和随机化概率的渐近性质，并据此近似功效等运行特征。
- 材料陈述的贡献
- 摘要称在二次方差函数的自然指数族结果模型下，展示分配数和随机化概率的渐近正态性，并以多种结果模型模拟评估近似准确度。
- 阅读关注点
- 应核对不确定性指标与分配规则、指数族条件、渐近近似适用的样本规模，以及功效近似与完整模拟之间的误差。
- 阅读边界
- 这是 arXiv 预印本，不能据此认定已同行评审或期刊接收；摘要未说明完整渐近条件、具体近似误差或有限样本错误率控制。
原文摘要
Bayesian response adaptive clinical trials are currently evaluating experimental therapies for several diseases. Adaptive decisions, such as pre-planned variations of the randomization probabilities, attempt to accelerate the development of new treatments. The design of response adaptive trials, in most cases, requires time consuming simulation studies to describe operating characteristics, such as type I/II error rates, across plausible scenarios. We investigate large sample approximations of pivotal operating characteristics in Bayesian Uncertainty directed trial Designs (BUDs). A BUD trial utilizes an explicit metric u to quantify the information accrued during the study on parameters of interest, for example the treatment effects. The randomization probabilities vary during time to minimize the uncertainty summary u at completion of the study. We provide an asymptotic analysis (i) of the allocation of patients to treatment arms and (ii) of the randomization probabilities. For BUDs with outcome distributions belonging to the natural exponential family with quadratic variance function, we illustrate the asymptotic normality of the number of patients assigned to each arm and of the randomization probabilities. We use these results to approximate relevant operating characteristics such as the power of the BUD. We evaluate the accuracy of the approximations through simulations under several scenarios for binary, time-to-event and continuous outcome models.