A Two-Part Framework for Estimating Individualized Treatment Rules From Semicontinuous Outcomes
从半连续结局估计个体化处理规则的两部分框架AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接研究根据患者特征选择处理的个体化规则，并用于卫生干预；零值与正值建模本身不足以另加分布效应分类。
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从半连续结局估计个体化处理规则的两部分框架
- 研究问题
- 医疗支付等结局同时包含大量零值和严格正值时，如何估计个体化处理规则，并在高维协变量下兼顾两个部分的信息？
- 方法与思路
- 作者分别针对零值部分与正值部分建立处理规则，使用联合变量选择且鼓励两部分系数同号的惩罚，并开发计算算法。
- 材料陈述的贡献
- 摘要称提出两部分处理规则框架及高效算法，证明估计与预测误差的 oracle 不等式，并以模拟和卫生系统干预展示效果。
- 阅读关注点
- 应核对规则优化的目标、处理效应识别条件、两部分如何组合，以及同号惩罚的科学依据和 oracle 不等式的前提。
- 阅读边界
- 依据版本未注明的 OpenAlex 索引摘要，不能推断刊本变化；摘要未说明处理分配机制或同号关系失效时的表现，不能泛化其有效性。
原文摘要
Jared D. Hulinga , Maureen A. Smithb & Guanhua Chenc* a Division of Biostatistics, University of Minnesota, Minneapolis, MN; b Departments of Population Health Sciences and Family Medicine, University of Wisconsin-Madison, Madison, WI; c Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WISupplementary materials for this article are available online. Please go to www.tandfonline.com/r/JASA.CONTACT Jared Huling huling@umn.edu Division of Biostatistics, University of Minnesota, Minneapolis, MN 55455; Guanhua Chen gchen25@wisc.edu Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI 53792.AbstractHealth care payments are an important component of health care utilization and are thus a major focus in health services and health policy applications. However, payment outcomes are semicontinuous in that over a given period of time some patients incur no payments and some patients incur large costs. Individualized treatment rules (ITRs) are a major part of the push for tailoring treatments and interventions to patients, yet there is a little work focused on estimating ITRs from semicontinuous outcomes. In this article, we develop a framework for estimation of ITRs based on two-part modeling, wherein the ITR is estimated by separately targeting the zero part of the outcome and the strictly positive part. To improve performance when high-dimensional covariates are available, we leverage a scientifically plausible penalty that simultaneously selects variables and encourages the signs of coefficients for each variable to agree between the two components of the ITR. We develop an efficient algorithm for computation and prove oracle inequalities for the resulting estimation and prediction errors. We demonstrate the effectiveness of our approach in simulated examples and in a study of a health system intervention. Supplementary materials for this article are available online.