Evidence from observational studies has become increasingly important for supporting healthcare policy making via cost-effectiveness (CE) analyses. Similar as in comparative effectiveness studies, health economic evaluations that consider subject-level heterogeneity produce individualized treatment rules (ITRs) 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 ITR (CE-ITR) 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 (NMB) to assess the trade-off between health benefits and related costs. We estimate CE-ITR 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 CE-ITR using NMB-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 (SPRINT) to illustrate the CE gains of assigning customized intensive blood pressure therapy.
翻译:与比较有效性研究一样,考虑到主题水平差异性的健康经济评价提出了个性化治疗规则(ITRs),往往比一刀切治疗更具成本效益,因此,根据因果推断框架开发统计工具,学习这种具有成本效益的ITR(CE-ITR),以便能够适当处理潜在的混杂问题,并可以适用于试验和观察研究。在本文件中,我们使用净货币效益概念评估健康利益和相关成本之间的交易。我们估计CE-ITR是病人特征的函数,一旦实施,通过最大限度地增加保健收益,最大限度地减少治疗费用,优化有限保健资源分配。我们采用有条件随机森林方法,并利用基于NMB的分类算法确定最佳的CE-ITR,其中提出两个分治型估算器,用于评估特定主题的重量,以便有效地纳入从检查机构所资助的最高血压中获取的信息。我们通过模拟研究,将我们获得的血压分析结果运用到我们的最高血压分析结果。