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Novel Designs and Methods to Remove Hidden Confounding Bias in Health Sciences

消除健康科学中隐藏的混杂偏差的新颖设计和方法

基本信息

批准号：
10678962
负责人：
Eric Joel Tchetgen Tchetgen
金额：
$ 46.95万
依托单位：
UNIVERSITY OF PENNSYLVANIA
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-05-15 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10678962
关键词：
AIDS prevention Address Adherence Africa Aging Air Pollution Alzheimer&apos s Disease Alzheimer&apos s disease related dementia Alzheimer&apos s disease risk Area Blood Pressure Body mass index Botswana Cluster randomized trial Data Diabetes Mellitus Disease Environmental Health Epidemiology Genetic HIV Health Health Sciences Incidence Interruption Intervention Learning Linkage Disequilibrium Literature Machine Learning Masks Mendelian randomization Methodology Methods Modeling Modernization Observational Study Outcome Participant Process Public Health Public Health Applications Research Randomized Research Design Research Personnel Risk Factors Series Social Sciences Testing Time ambient air pollution analytical method comparative design experimental study genetic variant high dimensionality intervention effect modifiable risk mortality novel pleiotropism semiparametric simulation theories treatment as prevention treatment effect uptake user friendly software

项目摘要

Abstract A major approach in causal inference literature aimed at mitigating bias due to unmeasured confounding is the so- called instrumental variable (IV) design which relies on identifying a variable which (i) influences the treatment process, (ii) has no direct effect on the outcome other than through the treatment, and (iii) is independent of any unmeasured confounder. IV methods are very well developed and widely used in social and health science, although validity of IV inferences may not be reliable if any of required assumptions (i)-(iii) is violated. This proposal aims to develop (a) new IV methods robust to violation of any of (i)-(iii); (b) New negative control methods that can be used to detect and sometimes to nonparametrically account for unmeasured confounding bias; (c) New bracketing methods for partial inference about causal effects in comparative interrupted time series studies. The proposed methods will be used to address current scientific queries in three major substantive public health areas:(1) to understand the health effects of air pollution; (2) to quantify the causal effects of modifiable risk factors for Alzheimer's disease and related disorders; (3) To uncover the mechanism by which a randomized package of interventions produced a substantial reduction of HIV incidence in a recent major cluster randomized trial of treatment as prevention in Botswana, Africa. Our proposal will provide the best available analytical methods to date to resolve confounding concerns in these high impact public health applications and more broadly in observational studies in the health sciences.

摘要在因果推理文献中，旨在减轻由于不可测量的混杂而导致的偏倚的一种主要方法是称为工具变量（IV）设计，其依赖于识别以下变量：（i）影响处理过程， (ii)除了通过治疗外，对结果没有直接影响，并且（iii）独立于任何未测量的混淆因素。IV方法非常发达，并广泛用于社会和健康科学，虽然有效性如果违反了所需假设（i）-（iii）中的任何一项，则IV推断可能不可靠。该提案旨在发展 (a)对违反（i）-（iii）中任一项具有鲁棒性的新IV方法;（B）可用于检测的新阴性对照方法有时是为了非参数化地解释未测量的混杂偏倚;（c）部分样本的新括号法中断时间序列比较研究中因果效应的推断。所提出的方法将用于解决目前在三个主要的实质性公共卫生领域的科学问题：（1）了解空气污染;（2）量化阿尔茨海默病和相关疾病的可改变风险因素的因果影响; (3)为了揭示一套随机干预措施大幅减少的机制，最近在非洲博茨瓦纳进行的一项以治疗为预防的大型随机分组试验中的艾滋病毒发病率。我们的建议将提供迄今为止最好的分析方法，以解决这些高影响公众的混淆问题。健康应用和更广泛的健康科学观察研究。