Bayesian clinical trials in action.
Bayesian clinical trials in action.
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DOI：
10.1002/sim.5404
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发表时间：
2012-11-10
影响因子：
2
通讯作者：
Chu, Caleb T.
中科院分区：
文献类型：
--
作者：
Lee, J. Jack;Chu, Caleb T.
关键词：
虽然自20世纪40年代以来，频率主义范式一直是临床试验设计的主要方法，但它有几个明显的局限性。替代贝叶斯范式已经大大增强了计算算法和计算机硬件的进步。与频率论框架相比，贝叶斯框架具有一些独特的优势，并且越来越频繁地将其纳入临床试验设计。使用广泛的文献综述来评估贝叶斯方法如何用于临床试验，我们发现它们最常用于剂量发现，疗效监测，毒性监测，诊断/决策，以及研究药代动力学/药效学。在临床试验中实施贝叶斯方法所需的额外基础设施可能包括运行研究设计、模拟和分析的专用软件程序，以及基于Web的应用程序，这些应用程序对于及时的数据输入和分析特别有用。试验的成功不仅需要开发适当的工具，还需要及时准确地执行数据输入、质量控制、自适应随机化和贝叶斯计算。贝叶斯和频率论方法的相对优点仍然是统计学中争论的主题。然而，可以找到更多的证据表明，至少在实际层面上，这两个阵营的观点是一致的。最终，更好的临床试验方法会导致更有效的设计，更低的样本量，更准确的结论，以及更好的结果。贝叶斯方法为更好的试验提供了有吸引力的替代方案。应设计和进行更多的此类试验，以完善这一方法，并证明其真实的效益。
Although the frequentist paradigm has been the predominant approach to clinical trial design since the 1940s, it has several notable limitations. The alternative Bayesian paradigm has been greatly enhanced by advancements in computational algorithms and computer hardware. Compared to its frequentist counterpart, the Bayesian framework has several unique advantages, and its incorporation into clinical trial design is occurring more frequently. Using an extensive literature review to assess how Bayesian methods are used in clinical trials, we find them most commonly used for dose finding, efficacy monitoring, toxicity monitoring, diagnosis/decision making, and for studying pharmacokinetics/pharmacodynamics. The additional infrastructure required for implementing Bayesian methods in clinical trials may include specialized software programs to run the study design, simulation, and analysis, and Web-based applications, which are particularly useful for timely data entry and analysis. Trial success requires not only the development of proper tools but also timely and accurate execution of data entry, quality control, adaptive randomization, and Bayesian computation. The relative merit of the Bayesian and frequentist approaches continues to be the subject of debate in statistics. However, more evidence can be found showing the convergence of the two camps, at least at the practical level. Ultimately, better clinical trial methods lead to more efficient designs, lower sample sizes, more accurate conclusions, and better outcomes for patients enrolled in the trials. Bayesian methods offer attractive alternatives for better trials. More such trials should be designed and conducted to refine the approach and demonstrate its real benefit in action.
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影响因子：
2
作者：
Chevret, Sylvie
通讯作者：
Chevret, Sylvie
影响因子：
2
作者：
Dmitrienko, A;Wang, MD
通讯作者：
Wang, MD
DOI：
10.1177/1740774509104992
发表时间：
2009-06
期刊：
Clinical trials (London, England)
影响因子：
--
作者：
Biswas S;Liu DD;Lee JJ;Berry DA
通讯作者：
Berry DA
影响因子：
6.7
作者：
Barker, A. D.;Sigman, C. C.;Esserman, L. J.
通讯作者：
Esserman, L. J.
影响因子：
45.3
作者：
Buzdar, AU;Ibrahim, NK;Hortobagyi, GN
通讯作者：
Hortobagyi, GN