Bayesian causal inference for environmental epidemiology data

环境流行病学数据的贝叶斯因果推断

基本信息

  • 批准号:
    RGPIN-2021-03187
  • 负责人:
  • 金额:
    $ 1.75万
  • 依托单位:
  • 依托单位国家:
    加拿大
  • 项目类别:
    Discovery Grants Program - Individual
  • 财政年份:
    2022
  • 资助国家:
    加拿大
  • 起止时间:
    2022-01-01 至 2023-12-31
  • 项目状态:
    已结题

项目摘要

The long term goal of the proposed research program is to develop Bayesian causal inference methods for environmental epidemiology data, with a specific emphasis on mediation analysis and longitudinal data. Bayesian causal inference is a thriving area of innovation in statistics, with important applications in environmental epidemiology. Environmental epidemiology is concerned with estimating the causal effects of environmental exposures (e.g. air pollution on human health), and to distinguish these effects from spurious associations due to confounding or other biases. Bayesian methods are well-suited to environmental epidemiology owing to their natural probability interpretation of uncertainty and their flexible handling of bias and missing data. The vision of the proposed research is to develop novel Bayesian causal inference methods that are motivated by important real-data applications and statistically valid.  The proposed methods will be available to data-analysts in environmental health sciences in particular, but also more generally in other domains including medicine, economics and the social sciences. The proposed research program is divided in three scientific objectives in Bayesian causal inference: 1) To extend my recent work on Bayesian methods for causal mediation analysis to account for missing data, with particular attention to environmental studies with missing biomarkers of environmental exposure, 2) To develop new Bayesian causal inference methods to explore sensitivity to bias from non-ignorable missing covariate and outcome data in longitudinal studies, and 3) To investigate novel methods for Bayesian quantile regression to detect heteroscedasticity and estimate heterogeneity of causal effects. The research will build on my recent progress, which includes developing new causal inference techniques that have been published in high-quality statistics journals and environmental epidemiology journals. A key component of the research will be to study the performance of new Bayesian methods compared to standard frequentist approaches, in terms of coverage probability, bias and average squared error. The methodological approach will include computer simulation experiments, analytical results based on simple models, and a study of performance when applied real datasets. Statistical codes using the software Stan will be made available in order to increase uptake of new methodologies. Trainees will be involved in all aspects of the research and gain knowledge in biostatistical methods applied to unique datasets.
拟议的研究计划的长期目标是开发环境流行病学数据的贝叶斯因果推理方法,特别强调中介分析和纵向数据。贝叶斯因果推理是统计学中一个蓬勃发展的创新领域,在环境流行病学中有重要应用。环境流行病学关注的是估计环境暴露的因果效应(例如空气污染对人类健康的影响),并将这些效应与由于混淆或其他偏见而产生的虚假关联区分开来。贝叶斯方法非常适合环境流行病学,因为它们对不确定性的自然概率解释以及它们对偏差和缺失数据的灵活处理。拟议的研究的愿景是开发新的贝叶斯因果推理方法,是由重要的实际数据应用和统计valid. The提出的方法将提供给数据分析师在环境健康科学,特别是在其他领域,包括医学,经济学和社会科学。在贝叶斯因果推理中,拟议的研究计划分为三个科学目标:1)扩展我最近关于因果中介分析的贝叶斯方法的工作,以解释缺失的数据,特别注意缺失环境暴露生物标志物的环境研究,2)开发新的贝叶斯因果推理方法,以探索对非因果关系偏倚的敏感性。纵向研究中可重复缺失的协变量和结果数据; 3)研究贝叶斯分位数回归的新方法,以检测异方差性并估计因果效应的异质性。这项研究将建立在我最近的进展,其中包括开发新的因果推理技术,这些技术已发表在高质量的统计学期刊和环境流行病学期刊上。研究的一个关键组成部分将是研究新的贝叶斯方法的性能相比,标准的频率论方法,在覆盖概率,偏差和平均平方误差。该方法将包括计算机模拟实验,基于简单模型的分析结果,以及应用真实的数据集时的性能研究。将提供使用Stan软件的统计代码,以增加对新方法的采用。学员将参与研究的各个方面,并获得应用于独特数据集的生物统计方法的知识。

项目成果

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McCandless, Lawrence其他文献

Profiles and Predictors of Environmental Chemical Mixture Exposure among Pregnant Women: The Health Outcomes and Measures of the Environment Study.
  • DOI:
    10.1021/acs.est.8b02946
  • 发表时间:
    2018-09-04
  • 期刊:
  • 影响因子:
    11.4
  • 作者:
    Kalloo, Geetika;Wellenius, Gregory A.;McCandless, Lawrence;Calafat, Antonia M.;Sjodin, Andreas;Karagas, Margaret;Chen, Aimin;Yolton, Kimberly;Lanphear, Bruce P.;Braun, Joseph M.
  • 通讯作者:
    Braun, Joseph M.
Chemical mixture exposures during pregnancy and cognitive abilities in school-aged children.
  • DOI:
    10.1016/j.envres.2021.111027
  • 发表时间:
    2021-06
  • 期刊:
  • 影响因子:
    8.3
  • 作者:
    Kalloo, Geetika;Wellenius, Gregory A.;McCandless, Lawrence;Calafat, Antonia M.;Sjodin, Andreas;Sullivan, Adam J.;Romano, Megan E.;Karagas, Margaret R.;Chen, Aimin;Yolton, Kimberly;Lanphear, Bruce P.;Braun, Joseph M.
  • 通讯作者:
    Braun, Joseph M.
Association of use of cleaning products with respiratory health in a Canadian birth cohort
  • DOI:
    10.1503/cmaj.190819
  • 发表时间:
    2020-02-18
  • 期刊:
  • 影响因子:
    14.6
  • 作者:
    Parks, Jaclyn;McCandless, Lawrence;Takaro, Tim K.
  • 通讯作者:
    Takaro, Tim K.

McCandless, Lawrence的其他文献

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{{ truncateString('McCandless, Lawrence', 18)}}的其他基金

Bayesian bias modelling for causal inference in statistics
统计学中因果推理的贝叶斯偏差模型
  • 批准号:
    RGPIN-2015-05155
  • 财政年份:
    2018
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian bias modelling for causal inference in statistics
统计学中因果推理的贝叶斯偏差模型
  • 批准号:
    RGPIN-2015-05155
  • 财政年份:
    2017
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian bias modelling for causal inference in statistics
统计学中因果推理的贝叶斯偏差模型
  • 批准号:
    RGPIN-2015-05155
  • 财政年份:
    2016
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian bias modelling for causal inference in statistics
统计学中因果推理的贝叶斯偏差模型
  • 批准号:
    RGPIN-2015-05155
  • 财政年份:
    2015
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Bias Modelling for analysis of observational data
用于分析观测数据的贝叶斯偏差模型
  • 批准号:
    371518-2009
  • 财政年份:
    2014
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Bias Modelling for analysis of observational data
用于分析观测数据的贝叶斯偏差模型
  • 批准号:
    371518-2009
  • 财政年份:
    2012
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Bias Modelling for analysis of observational data
用于分析观测数据的贝叶斯偏差模型
  • 批准号:
    371518-2009
  • 财政年份:
    2011
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Bias Modelling for analysis of observational data
用于分析观测数据的贝叶斯偏差模型
  • 批准号:
    371518-2009
  • 财政年份:
    2010
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual
Bayesian Bias Modelling for analysis of observational data
用于分析观测数据的贝叶斯偏差模型
  • 批准号:
    371518-2009
  • 财政年份:
    2009
  • 资助金额:
    $ 1.75万
  • 项目类别:
    Discovery Grants Program - Individual

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