Identification and Estimation of Causal Effects Using a Negative-Control Exposure in Time-Series Studies With Applications to Environmental Epidemiology
Yuanyuan Yu, Hongkai Li, Xiaoru Sun, Xinhui Liu, Fan Yang, Lei Hou, Lu Liu, Ran Yan, Yifan Yu, Ming Jing, Hao Xue, Wu‐Chun Cao, Qīng Wáng, Hua Zhong, Fuzhong Xue
Abstract
The initial aim of environmental epidemiology is to estimate the causal effects of environmental exposures on health outcomes. However, due to lack of enough covariates in most environmental data sets, current methods without enough adjustments for confounders inevitably lead to residual confounding. We propose a negative-control exposure based on a time-series studies (NCE-TS) model to effectively eliminate unobserved confounders using an after-outcome exposure as a negative-control exposure. We show that the causal effect is identifiable and can be estimated by the NCE-TS for continuous and categorical outcomes. Simulation studies indicate unbiased estimation by the NCE-TS model. The potential of NCE-TS is illustrated by 2 challenging applications: We found that living in areas with higher levels of surrounding greenness over 6 months was associated with less risk of stroke-specific mortality, based on the Shandong Ecological Health Cohort during January 1, 2010, to December 31, 2018. In addition, we found that the widely established negative association between temperature and cancer risks was actually caused by numbers of unobserved confounders, according to the Global Open Database from 2003-2012. The proposed NCE-TS model is implemented in an R package (R Foundation for Statistical Computing, Vienna, Austria) called NCETS, freely available on GitHub.