Efficient nonparametric estimation of heterogeneous treatment effects in causal inference

因果推理中异质治疗效果的有效非参数估计

基本信息

批准号：
10610947
负责人：
Luke Keele
金额：
$ 38.97万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-08-10 至 2026-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10610947
关键词：
Abdomen Age Benchmarking Binding Caring Clinical Clinical Research Comparative Effectiveness Research Complex Computer software Confidence Intervals Data Development Dimensions Educational workshop Effectiveness Emergency Situation Endometrial Carcinoma Ensure Healthcare Heterogeneity Hospital Nursing Intervention Machine Learning Masks Measures Methods Modeling Observational Study Operative Surgical Procedures Outcome Patient-Focused Outcomes Patients Performance Pharmaceutical Preparations Population Positioning Attribute Property Publishing Race Research Research Personnel Risk Sample Size Software Tools Statistical Methods Subgroup Testing Time Variant Work care outcomes clinical prognosis comparative effectiveness study design dosage experience flexibility high dimensionality improved individualized medicine innovation interest machine learning framework machine learning method non-compliance novel patient population patient subsets randomized trial semiparametric simulation software development study population theories tool treatment effect treatment strategy

项目摘要

PROJECT SUMMARY In nearly all studies of comparative effectiveness, the investigators seek to estimate how an in- tervention changes outcomes on average. That is, are outcomes for treated subjects better on average than for untreated subjects? While the average treatment effect (ATE) is a useful sum- mary of the treatment effect, the treatment effect may vary from patient to patient. The ATE is a low-dimensional summary of a treatment effect, since it summarizes the overall effect of the treatment using a single quantitative measure and ignores possible effect heterogeneity. Many in- vestigators seek to go beyond low-dimensional summaries by estimating heterogenous treatment effects (HTEs). The most common approach to the estimation of HTEs relies on simple statistical methods. Specifically, regression models are widely used but may be biased due to the linear functional form especially where HTEs that are nonlinear or based on complex combinations of patient subgroups. Currently, there is considerable interest in developing more flexible methods for the estimation of the HTEs. In this project, we will use the the doubly robust machine learning (DRML) framework to develop improved methods for a variety of HTEs. The DRML framework is a combination of semiparametric theory, machine learning (ML) methods, and doubly robust estimators. The key advantage of the DRML framework is that it allows one to reduce bias using ML estimation methods, while retaining the efficiency of parametric models.

项目概要在几乎所有比较有效性的研究中，研究人员都试图估计效果如何平均而言，干预会改变结果。也就是说，接受治疗的受试者的结果是否更好与未治疗受试者的平均水平相比？虽然平均治疗效果 (ATE) 是一个有用的总和- 尽管治疗效果不同，但治疗效果可能因患者而异。 ATE 是治疗效果的低维总结，因为它总结了治疗效果的整体效果使用单一定量测量进行治疗并忽略可能的效果异质性。许多在- 研究人员试图通过估计异质处理来超越低维总结效应（HTE）。估计 HTE 最常见的方法依赖于简单的统计方法。具体来说，回归模型被广泛使用，但由于线性回归模型可能存在偏差函数形式，特别是当 HTE 是非线性的或基于复杂的组合时患者亚组。目前，人们对开发更灵活的方法非常感兴趣用于估计 HTE。在这个项目中，我们将使用双鲁棒机器学习 (DRML) 框架，为各种 HTE 开发改进的方法。 DRML 框架是半参数理论、机器学习 (ML) 方法和双鲁棒性的结合估计器。 DRML 框架的主要优点是它允许人们使用以下方法来减少偏差： ML估计方法，同时保留参数模型的效率。