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

• 批准号: 10466912
• 负责人: Luke Keele
• 金额: $ 34.82万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 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/10466912
• 关键词: Abdomen; Age; Benchmarking; 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; base; 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

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.

项目摘要 在几乎所有的比较有效性研究中，研究人员试图估计一个在- 干预平均会改变结果。也就是说，接受治疗的受试者的结果是否更好， 与未接受治疗的受试者相比，平均值？虽然平均治疗效果（ATE）是一个有用的总和- 治疗效果的玛丽，治疗效果可能因患者而异。所述ATE被 治疗效果的低维总结，因为它总结了治疗的总体效果。 使用单一定量测量进行治疗，忽略了可能的效应异质性。很多人- 研究者试图通过估计异质性处理来超越低维摘要 影响（HTEs）。最常见的方法来估计热膨胀系数依赖于简单的统计 方法.具体而言，回归模型被广泛使用，但由于线性关系， 函数形式，特别是在非线性或基于以下复杂组合的HTE中 患者亚组。目前，人们对开发更灵活的方法有相当大的兴趣， 用于估计HTE。在这个项目中，我们将使用双重鲁棒机器学习 （DRML）框架来为各种HTE开发改进的方法。DRML框架 是半参数理论，机器学习（ML）方法和双重鲁棒性的组合 估计器。DRML框架的主要优点是，它允许使用 ML估计方法，同时保留参数模型的效率。