University of Leuven, Tiensestraat 102 Box 3713, 3000, Leuven, Belgium.
Leiden University, Leiden, The Netherlands.
Behav Res Methods. 2024 Dec;56(8):8259-8268. doi: 10.3758/s13428-024-02470-9. Epub 2024 Aug 20.
For many problems in clinical practice, multiple treatment alternatives are available. Given data from a randomized controlled trial or an observational study, an important challenge is to estimate an optimal decision rule that specifies for each client the most effective treatment alternative, given his or her pattern of pretreatment characteristics. In the present paper we will look for such a rule within the insightful family of classification trees. Unfortunately, however, there is dearth of readily accessible software tools for optimal decision tree estimation in the case of more than two treatment alternatives. Moreover, this primary tree estimation problem is also cursed with two secondary problems: a structural missingness in typical studies on treatment evaluation (because every individual is assigned to a single treatment alternative only), and a major issue of replicability. In this paper we propose solutions for both the primary and the secondary problems at stake. We evaluate the proposed solution in a simulation study, and illustrate with an application on the search for an optimal tree-based treatment regime in a randomized controlled trial on K = 3 different types of aftercare for younger women with early-stage breast cancer. We conclude by arguing that the proposed solutions may have relevance for several other classification problems inside and outside the domain of optimal treatment assignment.
对于许多临床实践中的问题，有多种治疗选择。鉴于随机对照试验或观察性研究的数据，一个重要的挑战是估计一个最优决策规则，该规则根据每个患者的预处理特征模式为其指定最有效的治疗选择。在本文中，我们将在分类树的有见地的家族中寻找这样的规则。然而，不幸的是，对于超过两种治疗选择的情况，缺乏易于访问的最优决策树估计的软件工具。此外，这个主要的树估计问题还存在两个次要问题：治疗评估的典型研究中存在结构缺失（因为每个个体仅被分配到一种治疗选择），以及一个主要的可重复性问题。在本文中，我们提出了解决主要和次要问题的方案。我们在模拟研究中评估了所提出的解决方案，并通过在随机对照试验中寻找基于树的最优治疗方案的应用来说明该问题，该试验涉及 3 种不同类型的早期乳腺癌年轻女性的康复后护理。最后我们认为，所提出的解决方案可能对最优治疗分配领域内外的其他几个分类问题具有相关性。
