Causal machine learning methods for studying the effects of environmental exposures on childhood cancer using natural experiments

使用自然实验研究环境暴露对儿童癌症影响的因果机器学习方法

基本信息

批准号：
10549353
负责人：
Rachel C Nethery
金额：
$ 13.91万
依托单位：
HARVARD SCHOOL OF PUBLIC HEALTH
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：
2021-02-01 至 2025-01-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10549353
关键词：
1,3-Butadiene Accounting Address Adult Affect Air Area Award Bayesian learning Benzene Biological Biology Cancer Biology Case/Control Studies Cellular biology Chemicals Child Child Health Childhood Leukemia Code Cohort Studies Communities Computer software DNA Sequence Alteration Data Data Science Developed Countries Development Discipline Educational workshop Ensure Environment Environmental Exposure Environmental Health Epidemiology Event Exposure to Foundations Funding Gasoline Goals Health Heterogeneity Home Incidence Investigation Knowledge Learning Literature Machine Learning Malignant Childhood Neoplasm Measures Medical Mentors Mentorship Methodology Methods Modeling Natural experiment Occupational Exposure Outcome Pediatric Oncology Play Positioning Attribute Predisposition Process Proliferating Regulation Reproducibility Research Research Design Resources Risk Factors Role Science Series Social Sciences Societies Source Time Time trend Training United States Environmental Protection Agency Work cancer epidemiology career development case control cohort educational atmosphere environmental agent epidemiology study exhaust experience experimental analysis health application health data innovation insight knowledge integration leukemia machine learning method machine learning model mortality neoplasm registry open source postnatal prenatal prenatal exposure programs simulation skills statistics symposium tool usability user-friendly

项目摘要

Project Summary: My goal is to build an independent research program in the development of causal inference methods for investigating environmental causes of childhood cancer. This K01 will enable me to conduct the focused, intensive research that will lay the groundwork for that program and to acquire the environmental, biological, and epidemiological training needed to maximize the rigor and impact of my work. Research: We propose to develop new causal machine learning (ML) methods that enable rigorous analysis of environmental natural experiments (NE) for estimation of the causal effects of environmental exposures on childhood cancer. Classical approaches to studying relationships between environmental exposures and childhood cancer are plagued with challenges and are yielding inconsistent findings. We contend that the recent proliferation of local environmental regulatory programs has created ample relevant NEs, which provide a powerful alternative approach to study these relationships. However, existing methods for NE analysis are poorly-suited for environmental health contexts. In particular, existing methods fail in the presence of rare outcomes like childhood cancer (Aim 1), and they are not able to provide insight into the timing at which children are most susceptible to any adverse exposure effects (Aim 2). We propose causal ML methods that overcome these challenges and apply them to a NE to study the effects of traffic-related air toxics on childhood leukemia. We also provide open source software implementing these methods (Aim 3). Career Development and Training: Given my extensive prior training and experience in statistics and data science, the primary aim of the training funded by this award will be the acquisition of subject-matter proficiency, which will provide me with the insights needed to create more effective and impactful environmental health methods. Specifically, I will pursue knowledge in the biology and epidemiology of childhood cancer and in environmental health and exposure biology. The training will be achieved through a combination of (1) hands-on collaborative research as described above; (2) intensive cross-disciplinary mentorship, with mentors specializing in environmental health, pediatric oncology, cancer biology and epidemiology, and statistics; (3) carefully-selected coursework in the Departments of Epidemiology, Environmental Health, and Cell Biology at Harvard; and (4) relevant conferences, workshops, and seminars. I will place special emphasis on establishing a network of expert collaborators in all my areas of training. Environment: The Harvard Medical Campus is home to the top research teams worldwide in both childhood cancer and environmental health. Due to Harvard’s position at the forefront of scientific discovery in these fields, its unparalleled resources, its vibrant intellectual atmosphere, and its promotion of collaborative science that integrates knowledge across disciplines, it provides an ideal environment in which to train on these topics.

项目概要：我的目标是建立一个独立的研究项目，开发因果推理方法这 K01 将使我能够进行有针对性的、深入研究将为该计划奠定基础并获得环境、生物、以及流行病学培训，以最大限度地提高我工作的严谨性和影响力。研究：我们建议开发新的因果机器学习（ML）方法，以实现严格的分析环境自然实验（NE），用于估计环境暴露对人体的因果影响研究环境暴露与儿童癌症之间关系的经典方法。儿童癌症面临挑战，并且得出的结果不一致。最近地方环境监管计划的激增创造了充足的相关 NE，这些 NE 提供了研究这些关系的强大替代方法然而，现有的 NE 分析方法还不够。特别是，现有方法不适用于环境健康环境。诸如儿童癌症（目标 1）之类的结果，并且他们无法深入了解发生这种情况的时间儿童最容易受到任何不良暴露影响（目标 2）。克服这些挑战并将其应用于NE来研究与交通相关的空气毒物对儿童的影响我们还提供实施这些方法的开源软件（目标 3）。职业发展和培训：考虑到我之前在统计和数据方面的广泛培训和经验科学，该奖项资助的培训的主要目的是获取主题熟练程度，这将为我提供创造更有效和有影响力所需的见解具体来说，我将寻求生物学和流行病学方面的知识。儿童癌症以及环境健康和暴露生物学方面的培训将通过 (1) 如上所述的实践合作研究的结合；(2) 密集的跨学科研究；导师制，导师专门从事环境健康、儿科肿瘤学、癌症生物学和 (3) 流行病学系精心挑选的课程，哈佛大学的环境健康和细胞生物学；以及 (4) 相关会议、讲习班和研讨会 I。将特别强调在我的所有培训领域建立专家合作者网络。环境：哈佛医学院是全球顶尖研究团队的所在地由于哈佛大学在这些领域的科学发现的前沿地位。领域、无与伦比的资源、充满活力的知识氛围以及对协作科学的促进它整合了跨学科的知识，为这些主题的培训提供了理想的环境。