当前位置： SCI文献检索 > ENVIRONMENTAL HEALTH PERSPECTIVES期刊下所有文献 > A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures.

A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures.

Abstract：

BACKGROUND: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 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 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 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 exposure effects. RESULTS:Quantile g-computation, unlike WQS regression, allows inference on mixture effects that is unbiased with appropriate CI 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 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. https://doi.org/10.1289/EHP5838.

journal_name

Environ Health Perspect

journal_title

Environmental health perspectives

authors

Keil AP,Buckley JP,O'Brien KM,Ferguson KK,Zhao S,White AJ

doi

10.1289/EHP5838

subject

Has Abstract

pub_date

2020-04-01 00:00:00

pages

47004

issue

eissn

0091-6765

issn

1552-9924

journal_volume

128

pub_type

杂志文章

在线工具

A Quantile-Based g-Computation Approach to Addressing the Effects of Exposure Mixtures.