整合决策建模和机器学习,为治疗分层提供信息。

IF 2 3区 医学 Q2 ECONOMICS
Health economics Pub Date : 2024-04-25 DOI:10.1002/hec.4834
David Glynn, John Giardina, Julia Hatamyar, Ankur Pandya, Marta Soares, Noemi Kreif
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引用次数: 0

摘要

人们越来越关注从 "一刀切(OSFA)"的方法转向分层治疗决策。了解预期疗效和成本效益如何随患者协变量的变化而变化是分层决策的一个关键方面。最近提出的机器学习(ML)方法可以在不预先指定亚组或函数形式的情况下学习结果的异质性,从而构建决策规则("政策"),将个体协变量映射到治疗决策中。然而,这些方法尚未将 ML 估计值整合到决策建模框架中,以反映与政策相关的长期结果并综合多种来源的信息。在本文中,我们提出了一种整合 ML 和决策建模的方法,即在有患者个人数据的情况下,估算特定治疗的生存时间。我们还提出了一种新颖的策略树算法实施方法,利用决策模型输出来定义子组。我们使用 SPRINT(收缩压干预试验)演示了这些方法,比较了 "标准 "和 "强化 "血压目标的治疗效果。我们发现,将 ML 纳入决策模型可影响 OSFA 政策的增量净健康效益 (INHB) 估计值。我们还发现有证据表明,使用基于树状算法定义的亚组对治疗进行分层可以提高 INHB 的估计值。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Integrating decision modeling and machine learning to inform treatment stratification

Integrating decision modeling and machine learning to inform treatment stratification

There is increasing interest in moving away from “one size fits all (OSFA)” approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules (‘policies’) that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for “standard” and “intensive” blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.

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来源期刊
Health economics
Health economics 医学-卫生保健
CiteScore
3.60
自引率
4.80%
发文量
177
审稿时长
4-8 weeks
期刊介绍: This Journal publishes articles on all aspects of health economics: theoretical contributions, empirical studies and analyses of health policy from the economic perspective. Its scope includes the determinants of health and its definition and valuation, as well as the demand for and supply of health care; planning and market mechanisms; micro-economic evaluation of individual procedures and treatments; and evaluation of the performance of health care systems. Contributions should typically be original and innovative. As a rule, the Journal does not include routine applications of cost-effectiveness analysis, discrete choice experiments and costing analyses. Editorials are regular features, these should be concise and topical. Occasionally commissioned reviews are published and special issues bring together contributions on a single topic. Health Economics Letters facilitate rapid exchange of views on topical issues. Contributions related to problems in both developed and developing countries are welcome.
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