A Bayesian credible subgroups approach to identifying patient subgroups with positive treatment effects.
A Bayesian credible subgroups approach to identifying patient subgroups with positive treatment effects.
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DOI：
10.1111/biom.12522
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发表时间：
2016-12
期刊：
影响因子：
1.9
通讯作者：
Carlin BP
中科院分区：
文献类型：
--
作者：
Schnell PM;Tang Q;Offen WW;Carlin BP
许多新的实验性治疗方法仅使一小部分人群受益。确定从这种治疗中受益的患者的基线协变量概况，而不是确定治疗是否具有人群水平的效果，可以大大降低进行临床试验的风险，并减少患者接受对他们无益的治疗。用于识别从实验治疗中受益的患者亚组的标准分析要么不考虑多重性，要么侧重于测试治疗-协变量相互作用的存在，而不是由此产生的个体化治疗效果。我们提出了一种贝叶斯可信子组方法来识别受益子组的两个边界子组：一个子组可能所有成员同时具有超过指定阈值的治疗效果，另一个可能没有成员这样做。我们通过模拟检查可信亚组方法的频率属性，并使用阿尔茨海默病治疗试验的数据说明该方法。最后，我们讨论了这种方法的优点和局限性，以识别对治疗有益的患者。
Many new experimental treatments benefit only a subset of the population. Identifying the baseline covariate profiles of patients who benefit from such a treatment, rather than determining whether or not the treatment has a population-level effect, can substantially lessen the risk in undertaking a clinical trial and expose fewer patients to treatments that do not benefit them. The standard analyses for identifying patient subgroups that benefit from an experimental treatment either do not account for multiplicity, or focus on testing for the presence of treatment-covariate interactions rather than the resulting individualized treatment effects. We propose a Bayesian credible subgroups method to identify two bounding subgroups for the benefiting subgroup: one for which it is likely that all members simultaneously have a treatment effect exceeding a specified threshold, and another for which it is likely that no members do. We examine frequentist properties of the credible subgroups method via simulations and illustrate the approach using data from an Alzheimer's disease treatment trial. We conclude with a discussion of the advantages and limitations of this approach to identifying patients for whom the treatment is beneficial.
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