Department of Population Health Sciences, 7060University of Utah, SLC, UT, USA.
21611Columbia University Medical Center, New York, NY, USA.
Stat Methods Med Res. 2022 Nov;31(11):2122-2136. doi: 10.1177/09622802221115876. Epub 2022 Aug 1.
Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules that are often more cost-effective than one-size-fits-all treatment. Thus, it is of great interest to develop statistical tools for learning such a cost-effective individualized treatment rule under the causal inference framework that allows proper handling of potential confounding and can be applied to both trials and observational studies. In this paper, we use the concept of net-monetary-benefit to assess the trade-off between health benefits and related costs. We estimate cost-effective individualized treatment rule as a function of patients' characteristics that, when implemented, optimizes the allocation of limited healthcare resources by maximizing health gains while minimizing treatment-related costs. We employ the conditional random forest approach and identify the optimal cost-effective individualized treatment rule using net-monetary-benefit-based classification algorithms, where two partitioned estimators are proposed for the subject-specific weights to effectively incorporate information from censored individuals. We conduct simulation studies to evaluate the performance of our proposals. We apply our top-performing algorithm to the NIH-funded Systolic Blood Pressure Intervention Trial to illustrate the cost-effectiveness gains of assigning customized intensive blood pressure therapy.
来自观察性研究的证据在通过成本效益分析支持医疗保健政策制定方面变得越来越重要。与比较效果研究类似，考虑个体差异的健康经济评估会产生个体化的治疗规则，这些规则通常比一刀切的治疗更具成本效益。因此，开发在因果推理框架下学习这种具有成本效益的个体化治疗规则的统计工具非常重要，该框架允许适当处理潜在的混杂因素，并可应用于试验和观察性研究。在本文中，我们使用净货币收益的概念来评估健康收益和相关成本之间的权衡。我们将成本效益个体化的治疗规则估计为患者特征的函数，当实施时，通过最大化健康收益同时最小化治疗相关成本来优化有限医疗资源的分配。我们采用条件随机森林方法，并使用基于净货币收益的分类算法来确定最优的成本效益个体化治疗规则，其中提出了两个分区估计器来有效地纳入来自截尾个体的信息。我们进行了模拟研究来评估我们建议的性能。我们将我们表现最好的算法应用于 NIH 资助的收缩压干预试验，以说明为定制强化血压治疗分配带来的成本效益收益。
