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Comparing Four Methods for Estimating Tree-Based Treatment Regimes.
International Journal of Biostatistics ( IF 1.2 ) Pub Date : 2017-05-20 , DOI: 10.1515/ijb-2016-0068
Aniek Sies 1 , Iven Van Mechelen 1
Affiliation  

When multiple treatment alternatives are available for a certain psychological or medical problem, an important challenge is to find an optimal treatment regime, which specifies for each patient the most effective treatment alternative given his or her pattern of pretreatment characteristics. The focus of this paper is on tree-based treatment regimes, which link an optimal treatment alternative to each leaf of a tree; as such they provide an insightful representation of the decision structure underlying the regime. This paper compares the absolute and relative performance of four methods for estimating regimes of that sort (viz., Interaction Trees, Model-based Recursive Partitioning, an approach developed by Zhang et al. and Qualitative Interaction Trees) in an extensive simulation study. The evaluation criteria were, on the one hand, the expected outcome if the entire population would be subjected to the treatment regime resulting from each method under study and the proportion of clients assigned to the truly best treatment alternative, and, on the other hand, the Type I and Type II error probabilities of each method. The method of Zhang et al. was superior regarding the first two outcome measures and the Type II error probabilities, but performed worst in some conditions of the simulation study regarding Type I error probabilities.

中文翻译:

比较四种估计基于树的处理方式的方法。

当针对某个心理或医学问题有多种治疗选择可用时,一项重要的挑战是找到一种最佳治疗方案,该方案针对每个患者,根据他或她的预处理特征模式,为其指定最有效的治疗选择。本文的重点是基于树的处理方案,该方案将最佳的处理替代方案与树的每片叶子联系在一起。因此,它们提供了对该制度基础决策结构的深刻见解。本文在广泛的模拟研究中比较了四种估计这种类型的状态的方法的绝对和相对性能(即,交互树,基于模型的递归分区,这是由Zhang等人开发的方法和定性交互树)。评估标准一方面是 如果整个人群都将接受由每种研究方法产生的治疗方案以及分配给真正最佳治疗替代方案的客户比例的预期结果,另一方面,则是每种人群的I型和II型错误概率方法。张等人的方法。在前两个结果度量和II型错误概率方面表现优异,但在有关I型错误概率的模拟研究的某些条件下表现最差。
更新日期:2019-11-01
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