Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis
Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis
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DOI：
10.1191/1740774505cn100oa
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发表时间：
2005-01-01
期刊：
影响因子：
2.7
通讯作者：
Berry, DA
中科院分区：
文献类型：
--
作者：
Berry, DA
贝叶斯方法及其在药物和医疗器械开发中的几个优势进行了描述。从分析的角度来看，一个优点是它提供了一种综合信息的方法。然而，采用贝叶斯方法设计临床试验可能比使用这种方法分析试验结果更有价值。贝叶斯方法提供了一种机制，用于更新试验期间积累的结果。这种更新可以完全、明确和前瞻性地纳入。使用贝叶斯方法的一个重要方式是根据当前结果计算未来结果的预测概率分布。我将展示如何利用预测分布来适应试验过程中积累的结果。可能的调整包括减少或增加样本量、删除治疗组以及根据中期结果修改各组的随机化比例。采用贝叶斯方法进行临床试验设计的结果是效率，试验中患者的治疗更好，主要终点的精确度更高。最后一个例子是早期和长期终点之间关系的贝叶斯建模。这样的建模也可以更早地做出决策。案例研究2和案例研究3分别涉及由于这种建模而缩短的和较小的试验。
The Bayesian approach and several of its advantages in drug and medical device development are described. One advantage from the perspective of analysis is that it provides a methodology for synthesizing information. However, taking a Bayesian approach to designing clinical trials is potentially more valuable than using this approach in analyzing trial results. Bayesian methodology provides a mechanism for updating what is known as results accumulate during a trial. Such updating can be incorporated completely explicitly and prospectively. An important way in which the Bayesian approach can be used is in calculating the predictive probability distribution of future results on the basis of current results. I show how to exploit predictive distributions in adapting to results that accumulate during the course of a trial. Possible adaptations including decreasing or increasing sample size, dropping treatment arms, and modifying the randomization proportions to the various arms depending on the interim results. Consequences of taking a Bayesian approach to clinical trial design are efficiency, better treatment of patients in the trial, and greater precision regarding the primary endpoints. An example of the last of these is Bayesian modeling of the relationship between early and longer term endpoints. Such modeling also enables earlier decision making. Case studies 2 and 3 deal with trials that were shorter and smaller, respectively, because of such modeling.