In the first stage of a two-stage study, the researcher uses a statistical model to impute the unobserved exposures. In the second stage, imputed exposures serve as covariates in epidemiological models. Imputation error in the first stage operate as measurement errors in the second stage, and thus bias exposure effect estimates. This study aims to improve the estimation of exposure effects by sharing information between the first and second stage. At the heart of our estimator is the observation that not all second-stage observations are equally important to impute. We thus borrow ideas from the optimal-experimental-design theory, to identify individuals of higher importance. We then improve the imputation of these individuals using ideas from the machine-learning literature of domain-adaptation. Our simulations confirm that the exposure effect estimates are more accurate than the current best practice. An empirical demonstration yields smaller estimates of PM effect on hyperglycemia risk, with tighter confidence bands. Sharing information between environmental scientist and epidemiologist improves health effect estimates. Our estimator is a principled approach for harnessing this information exchange, and may be applied to any two stage study.
A weighting method for simultaneous adjustment for confounding and joint exposure-outcome misclassifications
暴露和结果变量的联合错误分类可能导致因果关系确定-结果效应的流行病学研究中出现相当大的偏倚。在本文中，我们提出了一个新的极大似然估计的边际因果比值比，同时调整混杂和几种形式的联合误分类的暴露和结果变量。所提出的方法依赖于验证数据的建设的权重，占两个来源的偏见。加权估计量是Gravel和Platt（Statistics in Medicine，2018）提出的暴露误分类加权估计量的扩展，适用于再梗死数据。进行了模拟研究，研究其有限样本的性质，并将其与不考虑混杂或误分类的方法进行比较。新的估计显示出有利的大样本性能的模拟。需要进一步研究所提出的方法的敏感性，以及违反其假设的替代方案。在现有的R包中的一个新的R函数的估计的实施是方便的。
A Spectral Confounder Adjustment for Spatial Regression with Multiple Exposures and Outcomes
North Carolina State University（北卡罗来纳州立大学）；Colorado State University（科罗拉多州立大学）；US EPA, Center for Public Health and Environmental Assessment（美国环境保护署，公共健康与环境评估中心）
Educational Note: Paradoxical Collider Effect in the Analysis of Non-Communicable Disease Epidemiological Data: a reproducible illustration and web application
经典流行病学的重点是控制混杂，但直到最近，流行病学家才开始关注对撞机产生的偏倚。某对变量的碰撞器（例如，结果Y和暴露A）是由两者引起的第三变量（C）。在有向无环图（DAG）中，碰撞器是在反向分叉（即，A -> C <- Y中的变量C）。控制或调节碰撞器上的分析（即，通过分层或回归）可以在其原因之间引入虚假的关联。这可能解释了医学文献中许多自相矛盾的发现，其中特定结果的既定风险因素似乎具有保护作用。我们使用一个例子，从非传染性疾病流行病学的背景下，并解释条件作用的碰撞。我们生成了一个包含1,000个观察结果的数据集，并运行蒙特卡罗模拟来估计24小时膳食钠摄入量对收缩压的影响，控制年龄（作为混杂因素）和24小时尿蛋白排泄（作为碰撞器）。我们说明了如何添加一个碰撞器的回归模型引入偏见。因此，为了防止矛盾的关联，估计因果关系的流行病学家应该警惕条件碰撞。我们在整个手稿中提供了易于阅读的R代码框和GitHub存储库（https：//github.com/migariane/ColliderApp），供读者复制我们的示例。我们还提供了一个教育网络应用程序，允许实时交互，以可视化的矛盾效果的条件作用在对撞机http://watzilei.com/shiny/collider/。
Ground-Level Near Real-Time Modeling for PM2.5 Pollution Prediction
Oak Ridge National Laboratory（橡树岭国家实验室）；Johnson & Johnson（强生公司）；Los Alamos National Laboratory（洛斯阿拉莫斯国家实验室）；University of Southern California（南加州大学）；University of Utah（犹他大学）
Modelling the Spatially Varying Non-Linear Effects of Heat Exposure
MRC Centre for Environment and Health, School of Public Health, Imperial College London（帝国理工学院公共卫生学院环境与健康医学研究委员会中心）；Grantham Institute for Climate Change and the Environment, Imperial College London（帝国理工学院格兰瑟姆气候变化与环境研究所）
Integrating Temporal Disaggregation and Distributed Lag Nonlinear Models for Bayesian Spatio-Temporal Disease Mapping with High-Resolution Environmental Exposures
KU Leuven（荷语鲁汶大学）；Hasselt University（哈瑟尔特大学）；Clinton Health Access Initiative（克林顿健康准入倡议）；National Malaria Control Program, Ministry of Health（卫生部国家疟疾控制项目）；University of Birmingham（伯明翰大学）
