Estimation of treatment effect in a subpopulation: An empirical Bayes approach.
Estimation of treatment effect in a subpopulation: An empirical Bayes approach.
复制标题
DOI：
10.1080/10543406.2015.1052480
复制
发表时间：
2016
影响因子：
1.1
通讯作者：
Jeong J
中科院分区：
文献类型：
--
作者：
Shen C;Li X;Jeong J
众所周知，由于患者的异质性，医疗干预的益处可能不会均匀地分布在目标人群中，并且基于传统随机临床试验的结论可能并不适用于每个人。鉴于随机试验的成本不断增加以及招募患者的困难，迫切需要开发分析方法来估计亚人群的治疗效果。特别是，由于亚人群的样本量有限以及需要进行多重比较，标准分析往往会产生治疗效果的宽置信区间，而这些置信区间通常是无信息的。我们提出了一种经验贝叶斯方法，将目标亚群中嵌入的信息和来自其他受试者的信息结合起来，以构建治疗效果的置信区间。该方法在描述真实治疗效果的不确定性方面以其简单性和有形性而吸引人。提出了模拟研究和真实数据分析。
It is well recognized that the benefit of a medical intervention may not be distributed evenly in the target population due to patient heterogeneity and conclusions based on conventional randomized clinical trials may not apply to every person. Given the increasing cost of randomized trials and difficulties in recruiting patients, there is a strong need to develop analytical approaches to estimate treatment effect in sub-populations. In particular, due to limited sample size for sub-populations and the need for multiple comparisons, standard analysis tends to yield wide confidence intervals of the treatment effect that are often non-informative. We propose an empirical Bayes approach to combine both information embedded in a target sub-population and information from other subjects to construct confidence intervals of the treatment effect. The method is appealing in its simplicity and tangibility in characterizing the uncertainty about the true treatment effect. Simulation studies and a real data analysis are presented.