Bayesian causal inference for environmental epidemiology data

该研究计划的长期目标是为环境流行病学数据开发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着重要的应用。环境流行病学涉及估计环境暴露（例如空气污染对人类健康）的因果影响，并将这些影响与由于混淆或其他偏差造成的虚假关联区分开来。贝叶斯方法由于其对不确定性的自然概率解释以及对偏差和缺失数据的灵活处理而非常适合于环境流行病学。提出的研究的愿景是开发新的贝叶斯因果推理方法，这些方法是由重要的实际数据应用和统计有效的驱动的。拟议的方法将特别提供给环境卫生科学领域的数据分析人员，但也更广泛地提供给包括医学、经济学和社会科学在内的其他领域的数据分析人员。在贝叶斯因果推理中，提出的研究计划分为三个科学目标：1)扩展我最近在贝叶斯方法因果中介分析方面的工作，以解释缺失的数据，特别关注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，以探索纵向研究中不可忽视的缺失协变量和结果数据对偏差的敏感性；3)研究贝叶斯分位数回归检测异方差和估计因果效应异质性的新方法。这项研究将以我最近的进展为基础，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期刊和环境流行病学期刊上。该研究的一个关键组成部分将是研究新贝叶斯方法在覆盖概率、偏差和平均平方误差方面与标准频率学方法的性能。方法方法将包括计算机模拟实验，基于简单模型的分析结果，以及应用真实数据集时的性能研究。将提供使用Stan软件的统计代码，以便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。

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.