A quantile-based g-computation approach to addressing the effects of exposure mixtures

Alexander P. Keil, Jessie P. Buckley, Katie M. O’Brien, Kelly K. Ferguson, Shanshan Zhao, Alexandra J. White

Research output: Contribution to journalArticlepeer-review

Abstract

BACKGROUND: Exposure mixtures frequently occur in data across many domains, particularly in the fields of environmental and nutritional epidemiol- ogy. Various strategies have arisen to answer questions about exposure mixtures, including methods such as weighted quantile sum (wQS) regression that estimate a joint effect of the mixture components. OBJECTIVES: We demonstrate a new approach to estimating the joint effects of a mixture: quantile g-computation. This approach combines the infer- ential 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 the effects of mixtures in a variety of common scenarios. METHODS: We examine the bias, confidence interval (CI) coverage, and bias-variance tradeoff of quantile g-computation and wQS regression and how these quantities are impacted by the presence of noncausal exposures, exposure correlation, unmeasured confounding, and nonlinearity of expo- sure effects. RESULTS: Quantile g-computation, unlike wQS regression, allows inference on mixture effects that is unbiased with appropriate CI coverage at sam- ple sizes typically encountered in epidemiologic studies and when the assumptions of wQS regression are not met. Further, wQS regression can mag- nify bias from unmeasured confounding that might occur if important components of the mixture are omitted from the analysis. DISCUSSION: Unlike inferential approaches that examine the effects of individual exposures while holding other exposures constant, methods like quantile g-computation that can estimate the effect of a mixture are essential for understanding the 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.

Original languageEnglish (US)
Article number047004
JournalEnvironmental health perspectives
Volume128
Issue number4
DOIs
StatePublished - 2020

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

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