Cargando…
Identification of confounder in epidemiologic data contaminated by measurement error in covariates
BACKGROUND: Common methods for confounder identification such as directed acyclic graphs (DAGs), hypothesis testing, or a 10 % change-in-estimate (CIE) criterion for estimated associations may not be applicable due to (a) insufficient knowledge to draw a DAG and (b) when adjustment for a true confou...
Autores principales: | , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4870765/ https://www.ncbi.nlm.nih.gov/pubmed/27193095 http://dx.doi.org/10.1186/s12874-016-0159-6 |
_version_ | 1782432493842989056 |
---|---|
author | Lee, Paul H. Burstyn, Igor |
author_facet | Lee, Paul H. Burstyn, Igor |
author_sort | Lee, Paul H. |
collection | PubMed |
description | BACKGROUND: Common methods for confounder identification such as directed acyclic graphs (DAGs), hypothesis testing, or a 10 % change-in-estimate (CIE) criterion for estimated associations may not be applicable due to (a) insufficient knowledge to draw a DAG and (b) when adjustment for a true confounder produces less than 10 % change in observed estimate (e.g. in presence of measurement error). METHODS: We compare previously proposed simulation-based approach for confounder identification that can be tailored to each specific study and contrast it with commonly applied methods (significance criteria with cutoff levels of p-values of 0.05 or 0.20, and CIE criterion with a cutoff of 10 %), as well as newly proposed two-stage procedure aimed at reduction of false positives (specifically, risk factors that are not confounders). The new procedure first evaluates potential for confounding by examination of correlation of covariates and applies simulated CIE criteria only if there is evidence of correlation, while rejecting a covariate as confounder otherwise. These approaches are compared in simulations studies with binary, continuous, and survival outcomes. We illustrate the application of our proposed confounder identification strategy in examining the association of exposure to mercury in relation to depression in the presence of suspected confounding by fish intake using the National Health and Nutrition Examination Survey (NHANES) 2009–2010 data. RESULTS: Our simulations showed that the simulation-determined cutoff was very sensitive to measurement error in exposure and potential confounder. The analysis of NHANES data demonstrated that if the noise-to-signal ratio (error variance in confounder/variance of confounder) is at or below 0.5, roughly 80 % of the simulated analyses adjusting for fish consumption would correctly result in a null association of mercury and depression, and only an extremely poorly measured confounder is not useful to adjust for in this setting. CONCLUSIONS: No a prior criterion developed for a specific application is guaranteed to be suitable for confounder identification in general. The customization of model-building strategies and study designs through simulations that consider the likely imperfections in the data, as well as finite-sample behavior, would constitute an important improvement on some of the currently prevailing practices in confounder identification and evaluation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-016-0159-6) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-4870765 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48707652016-05-19 Identification of confounder in epidemiologic data contaminated by measurement error in covariates Lee, Paul H. Burstyn, Igor BMC Med Res Methodol Research Article BACKGROUND: Common methods for confounder identification such as directed acyclic graphs (DAGs), hypothesis testing, or a 10 % change-in-estimate (CIE) criterion for estimated associations may not be applicable due to (a) insufficient knowledge to draw a DAG and (b) when adjustment for a true confounder produces less than 10 % change in observed estimate (e.g. in presence of measurement error). METHODS: We compare previously proposed simulation-based approach for confounder identification that can be tailored to each specific study and contrast it with commonly applied methods (significance criteria with cutoff levels of p-values of 0.05 or 0.20, and CIE criterion with a cutoff of 10 %), as well as newly proposed two-stage procedure aimed at reduction of false positives (specifically, risk factors that are not confounders). The new procedure first evaluates potential for confounding by examination of correlation of covariates and applies simulated CIE criteria only if there is evidence of correlation, while rejecting a covariate as confounder otherwise. These approaches are compared in simulations studies with binary, continuous, and survival outcomes. We illustrate the application of our proposed confounder identification strategy in examining the association of exposure to mercury in relation to depression in the presence of suspected confounding by fish intake using the National Health and Nutrition Examination Survey (NHANES) 2009–2010 data. RESULTS: Our simulations showed that the simulation-determined cutoff was very sensitive to measurement error in exposure and potential confounder. The analysis of NHANES data demonstrated that if the noise-to-signal ratio (error variance in confounder/variance of confounder) is at or below 0.5, roughly 80 % of the simulated analyses adjusting for fish consumption would correctly result in a null association of mercury and depression, and only an extremely poorly measured confounder is not useful to adjust for in this setting. CONCLUSIONS: No a prior criterion developed for a specific application is guaranteed to be suitable for confounder identification in general. The customization of model-building strategies and study designs through simulations that consider the likely imperfections in the data, as well as finite-sample behavior, would constitute an important improvement on some of the currently prevailing practices in confounder identification and evaluation. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12874-016-0159-6) contains supplementary material, which is available to authorized users. BioMed Central 2016-05-18 /pmc/articles/PMC4870765/ /pubmed/27193095 http://dx.doi.org/10.1186/s12874-016-0159-6 Text en © Lee and Burstyn. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Lee, Paul H. Burstyn, Igor Identification of confounder in epidemiologic data contaminated by measurement error in covariates |
title | Identification of confounder in epidemiologic data contaminated by measurement error in covariates |
title_full | Identification of confounder in epidemiologic data contaminated by measurement error in covariates |
title_fullStr | Identification of confounder in epidemiologic data contaminated by measurement error in covariates |
title_full_unstemmed | Identification of confounder in epidemiologic data contaminated by measurement error in covariates |
title_short | Identification of confounder in epidemiologic data contaminated by measurement error in covariates |
title_sort | identification of confounder in epidemiologic data contaminated by measurement error in covariates |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4870765/ https://www.ncbi.nlm.nih.gov/pubmed/27193095 http://dx.doi.org/10.1186/s12874-016-0159-6 |
work_keys_str_mv | AT leepaulh identificationofconfounderinepidemiologicdatacontaminatedbymeasurementerrorincovariates AT burstynigor identificationofconfounderinepidemiologicdatacontaminatedbymeasurementerrorincovariates |