Format

Send to

Choose Destination
Curr Epidemiol Rep. 2018 Jun;5(2):160-165. doi: 10.1007/s40471-018-0145-0. Epub 2018 Apr 5.

Environmental exposure mixtures: questions and methods to address them.

Author information

1
Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, MD, USA.
2
Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Health, MD, USA.

Abstract

Purpose of this review:

This review provides a summary of statistical approaches that researchers can use to study environmental exposure mixtures. Two primary considerations are the form of the research question and the statistical tools best suited to address that question. Because the choice of statistical tools is not rigid, we make recommendations about when each tool may be most useful.

Recent Findings:

When dimensionality is relatively low, some statistical tools yield easily interpretable estimates of effect (e.g., risk ratio, odds ratio) or intervention impacts. When dimensionality increases, it is often necessary to compromise this interpretablity in favor of identifying interesting statistical signals from noise; this requires applying statistical tools that are oriented more heavily towards dimension reduction via shrinkage and/or variable selection.

Summary:

The study of complex exposure mixtures has prompted development of novel statistical methods. We suggest that further validation work would aid practicing researchers in choosing among existing and emerging statistical tools for studying exposure mixtures.

KEYWORDS:

Bayesian methods; complex mixtures; environmental epidemiology; machine learning

PMID:
30643709
PMCID:
PMC6329601
[Available on 2019-06-01]
DOI:
10.1007/s40471-018-0145-0

Supplemental Content

Loading ...
Support Center