Causal Inference Analysis for Poorly Soluble Low Toxicity Particles, Lung Function, and Malignancy
AffiliationMel and Enid Zuckerman College of Public Health, University of Arizona
causal inference analysis
chronic obstructive pulmonary disease (COPD)
directed acyclic graph
MetadataShow full item record
PublisherFrontiers Media S.A.
CitationHarber, P. (2022). Causal Inference Analysis for Poorly Soluble Low Toxicity Particles, Lung Function, and Malignancy. Frontiers in Public Health, 10.
JournalFrontiers in Public Health
RightsCopyright © 2022 Harber. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractPoorly soluble low toxicity particles such as carbon black and titanium dioxide have raised concern about possible nonmalignant and malignant pulmonary effects. This paper illustrates application of causal inference analysis to assessing these effects. A framework for analysis is created using directed acyclic graphs to define pathways from exposure to potential lung cancer or chronic airflow obstruction outcomes. Directed acyclic graphs define influences of confounders, backdoor pathways, and analytic models. Potential mechanistic pathways such as intermediate pulmonary inflammation are illustrated. An overview of available data for each of the inter-node links is presented. Individual empirical epidemiologic studies have limited ability to confirm mechanisms of potential causal relationships due to the complexity of causal pathways and the extended time course over which disease may develop. Therefore, an explicit conceptual and graphical framework to facilitate synthesizing data from several studies to consider pulmonary inflammation as a common pathway for both chronic airflow obstruction and lung cancer is suggested. These methods are useful to clarify potential bona fide and artifactual observed relationships. They also delineate variables which should be included in analytic models for single study data and biologically relevant variables unlikely to be available from a single study. Copyright © 2022 Harber.
NoteOpen access journal
VersionFinal published version
Except where otherwise noted, this item's license is described as Copyright © 2022 Harber. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY).
- Causal inference in cumulative risk assessment: The roles of directed acyclic graphs.
- Authors: Brewer LE, Wright JM, Rice G, Neas L, Teuschler L
- Issue date: 2017 May
- Genetic regulation of gene expression in the lung identifies CST3 and CD22 as potential causal genes for airflow obstruction.
- Authors: Lamontagne M, Timens W, Hao K, Bossé Y, Laviolette M, Steiling K, Campbell JD, Couture C, Conti M, Sherwood K, Hogg JC, Brandsma CA, van den Berge M, Sandford A, Lam S, Lenburg ME, Spira A, Paré PD, Nickle D, Sin DD, Postma DS
- Issue date: 2014 Nov
- Signed directed acyclic graphs for causal inference.
- Authors: VanderWeele TJ, Robins JM
- Issue date: 2010 Jan 1
- Predictive models aren't for causal inference.
- Authors: Arif S, MacNeil MA
- Issue date: 2022 Aug
- [Causal analysis approaches in epidemiology].
- Authors: Dumas O, Siroux V, Le Moual N, Varraso R
- Issue date: 2014 Feb