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当目的不正当手段时:学习谁被预测会产生有害的间接影响

Kara E Rudolph¹, Iván Díaz²

¹Department of Epidemiology, Mailman School of Public Health, Columbia University.

Journal of the Royal Statistical Society. Series A, (Statistics in Society)

|July 3, 2023

概括

这项研究引入了一种新的方法来识别可能经历治疗有害间接影响的个体,即使整体治疗效果是有益的. 这有助于为特定子组做出明智的治疗决定.

科学领域:

因果推理的原因推理.
生物统计学生物统计学
卫生经济学卫生经济学

背景情况:

最佳治疗规则旨在通过根据个体特征量身定制干预措施来最大限度地提高预期结果.
识别潜在有害的间接治疗效应的子组对于个性化医学和道德考虑至关重要.
现有的方法可能无法充分解决中介分析中的治疗后混因素或分布假设.

研究的目的:

提出一种新的非参数方法,用于识别具有预测有害间接治疗效应的子组.
在调解分析和最佳治疗规则方面的现有文献基础上建立.
开发一种灵活的方法,包括处理后的混因素,避免限制性分布假设.

主要方法:

使用非参数方法来识别具有有害间接影响的子组.
该方法结合了中介者-结果关系的治疗后混因素.
在基线共变量,介导变量或结果的分布上没有限制.

主要成果:

提出的方法成功地在MTO住房券实验中确定了一个男孩子组.
预计这一小组将经历收到住房券对精神疾病发病率的有害间接影响.
有害的间接影响通过学校和社区环境调解员运作.

结论:

开发的方法提供了一个强大的工具,用于识别通过间接途径治疗可能受到不利影响的个人.
这种方法可以通过突出特定亚群的潜在风险来为临床决策和公共卫生干预提供信息.
对MTO实验的应用证明了该方法在现实环境中的实际实用性.

关键词:

有关因果推理的推理.进行交叉验证.动态处理制度动态处理制度干预间接影响的间接影响.调解调解是一种调解方式.最佳的个性化治疗方案.最好的规则是最优的规则.

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