3 research outputs found
A quantile-based g-computation approach to addressing the effects of exposure mixtures
Exposure mixtures frequently occur in data across many domains, particularly
in the fields of environmental and nutritional epidemiology. Various strategies
have arisen to answer questions about mixtures, including methods such as
weighted quantile sum (WQS) regression that estimate a joint effect of the
mixture components.We demonstrate a new approach to estimating the joint
effects of a mixture: quantile g-computation. This approach combines the
inferential simplicity of WQS regression with the flexibility of g-computation,
a method of causal effect estimation. We use simulations to examine whether
quantile g-computation and WQS regression can accurately and precisely estimate
effects of mixtures in common scenarios. We examine the bias, confidence
interval coverage, and bias-variance tradeoff of quantile g-computation and WQS
regression, and how these quantities are impacted by the presence of non-causal
exposures, exposure correlation, unmeasured confounding, and non-linear
effects. Quantile g-computation, unlike WQS regression allows inference on
mixture effects that is unbiased with appropriate confidence interval coverage
at sample sizes typically encountered in epidemiologic studies and when the
assumptions of WQS regression are not met. Further, WQS regression can magnify
bias from unmeasured confounding that might occur if important components of
the mixture are omitted. Unlike inferential approaches that examine effects of
individual exposures, methods like quantile g-computation that can estimate the
effect of a mixture are essential for understanding effects of potential public
health actions that act on exposure sources. Our approach may serve to help
bridge gaps between epidemiologic analysis and interventions such as
regulations on industrial emissions or mining processes, dietary changes, or
consumer behavioral changes that act on multiple exposures simultaneously.Comment: Main manuscript (3 figures, 4 tables, 7000 words) + appendi