Optimal Dynamic Regimes: Presenting a Case for Predictive Inference
Optimal Dynamic Regimes: Presenting a Case for Predictive Inference
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DOI：
10.2202/1557-4679.1204
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发表时间：
2010-01-01
影响因子：
1.2
通讯作者：
Saarela, Olli
中科院分区：
文献类型：
--
作者：
Arjas, Elja;Saarela, Olli
动态治疗方案是一种决策规则，其中个体在任何给定时间的治疗选择可以取决于该个体的已知既往史，包括基线协变量、早期治疗及其测量的反应。在本文中，我们认为，至少在相当简单的情况下，找到最佳方案可以通过直接应用非参数贝叶斯建模和预测推理来实现。作为一个例子，我们认为一个推理问题的一个子集的多中心艾滋病队列研究（MACS）的数据集，研究AZT启动对未来的CD 4-细胞计数在12个月的后续影响。
Dynamic treatment regime is a decision rule in which the choice of the treatment of an individual at any given time can depend on the known past history of that individual, including baseline covariates, earlier treatments, and their measured responses. In this paper we argue that finding an optimal regime can, at least in moderately simple cases, be accomplished by a straightforward application of nonparametric Bayesian modeling and predictive inference. As an illustration we consider an inference problem in a subset of the Multicenter AIDS Cohort Study (MACS) data set, studying the effect of AZT initiation on future CD4-cell counts during a 12-month follow-up.