Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding
Effective sample size for computing prior hyperparameters in Bayesian phase I-II dose-finding
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DOI：
10.1177/1740774514547397
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发表时间：
2014-12-01
期刊：
影响因子：
2.7
通讯作者：
Norris, J. Clift
中科院分区：
文献类型：
--
作者：
Thall, Peter F.;Herrick, Richard C.;Norris, J. Clift
背景：基于疗效毒性权衡的设计是一种实用的贝叶斯I-II期剂量探索方法。由于设计的性能对先验超参数和目标权衡轮廓的形状非常敏感，因此正确指定这两个设计元素是必不可少的。目的：目标是提供一种方法，该方法使用引发的平均结果概率来导出既不过度信息化也不过度分散的先验，以及指定目标权衡轮廓的实用指南。方法：提出了一种确定先验超参数的通用算法，该算法使用最小二乘惩罚有效样本大小。提供了用于指定权衡轮廓的准则。这些方法通过晚期前列腺癌的临床试验来说明。提供了一个新版本的功效毒性程序的实施。结果：算法和指南一起提供了设计的操作特性的实质性改进。限制：该方法需要大量的引出值和设计参数，并且需要计算机模拟才能获得可接受的设计。结论：这两个关键的改进大大提高了功效毒性设计的实际用途，并直接使用更新的计算机程序来实现。用于确定先验超参数以确保特定信息水平的算法是通用的，并且可以应用于除有效性毒性方法基础之外的模型。
Background: The efficacy toxicity trade-off based design is a practical Bayesian phase I-II dose-finding methodology. Because the design's performance is very sensitive to prior hyperparameters and the shape of the target trade-off contour, specifying these two design elements properly is essential.Purpose: The goals are to provide a method that uses elicited mean outcome probabilities to derive a prior that is neither overly informative nor overly disperse, and practical guidelines for specifying the target trade-off contour.Methods: A general algorithm is presented that determines prior hyperparameters using least squares penalized by effective sample size. Guidelines for specifying the trade-off contour are provided. These methods are illustrated by a clinical trial in advanced prostate cancer. A new version of the efficacy toxicity program is provided for implementation.Results: Together, the algorithm and guidelines provide substantive improvements in the design's operating characteristics.Limitations: The method requires a substantial number of elicited values and design parameters, and computer simulations are required to obtain an acceptable design.Conclusion: The two key improvements greatly enhance the efficacy toxicity design's practical usefulness and are straightforward to implement using the updated computer program. The algorithm for determining prior hyperparameters to ensure a specified level of informativeness is general, and may be applied to models other than that underlying the efficacy toxicity method.