The control of confounding by intermediate variables
Citations Over TimeTop 10% of 1989 papers
Abstract
In epidemiologic studies of the effect of an exposure on disease, the crude association of exposure with disease may fail to reflect a causal association due to confounding by one or more covariates. Most previous discussions of confounding in the epidemiologic literature have considered only point exposure studies, that is, studies that measure exposure and covariate status only once, at start of follow-up. In this paper we offer definitions of confounding suitable for longitudinal studies that obtain data on exposure, covariate, and vital status at several points in time. An important difference between longitudinal studies and point exposure studies is that, in longitudinal studies, a time-dependent covariate can be simultaneously a confounder and an intermediate variable on the causal pathway from exposure to disease. In this paper I propose an estimator, the extended standardized risk difference, that provides control for confounding by a covariate that is simultaneously a confounder and an intermediate variable.
Related Papers
- → An introduction to the full random effects model(2022)21 cited
- → Compensation and Amplification of Attenuation Bias in Causal Effect Estimates(2019)10 cited
- → Sparse classification with paired covariates(2019)7 cited
- Survival analysis with coarsely observed covariates(2003)
- → Adjustment when Covariates are Fallible(2016)6 cited