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Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies
OBJECTIVES: In observational studies, epidemiologists often attempt to estimate the total effect of an exposure on an outcome of interest. However, when the underlying diagram is unknown and limited knowledge is available, dissecting bias performances is essential to estimating the total effect of a...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719285/ https://www.ncbi.nlm.nih.gov/pubmed/29162569 http://dx.doi.org/10.1136/bmjopen-2016-015640 |
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author | Wang, Tingting Li, Hongkai Su, Ping Yu, Yuanyuan Sun, Xiaoru Liu, Yi Yuan, Zhongshang Xue, Fuzhong |
author_facet | Wang, Tingting Li, Hongkai Su, Ping Yu, Yuanyuan Sun, Xiaoru Liu, Yi Yuan, Zhongshang Xue, Fuzhong |
author_sort | Wang, Tingting |
collection | PubMed |
description | OBJECTIVES: In observational studies, epidemiologists often attempt to estimate the total effect of an exposure on an outcome of interest. However, when the underlying diagram is unknown and limited knowledge is available, dissecting bias performances is essential to estimating the total effect of an exposure on an outcome when mistakenly adjusting for mediators under logistic regression. Through simulation, we focused on six causal diagrams concerning different roles of mediators. Sensitivity analysis was conducted to assess the bias performances of varying across exposure-mediator effects and mediator-outcome effects when adjusting for the mediator. SETTING: Based on the causal relationships in the real world, we compared the biases of varying across the effects of exposure-mediator with those of varying across the effects of mediator-outcome when adjusting for the mediator. The magnitude of the bias was defined by the difference between the estimated effect (using logistic regression) and the total effect of the exposure on the outcome. RESULTS: In four scenarios (a single mediator, two series mediators, two independent parallel mediators or two correlated parallel mediators), the biases of varying across the effects of exposure-mediator were greater than those of varying across the effects of mediator-outcome when adjusting for the mediator. In contrast, in two other scenarios (a single mediator or two independent parallel mediators in the presence of unobserved confounders), the biases of varying across the effects of exposure-mediator were less than those of varying across the effects of mediator-outcome when adjusting for the mediator. CONCLUSIONS: The biases were more sensitive to the variation of effects of exposure-mediator than the effects of mediator-outcome when adjusting for the mediator in the absence of unobserved confounders, while the biases were more sensitive to the variation of effects of mediator-outcome than those of exposure-mediator in the presence of an unobserved confounder. |
format | Online Article Text |
id | pubmed-5719285 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-57192852017-12-08 Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies Wang, Tingting Li, Hongkai Su, Ping Yu, Yuanyuan Sun, Xiaoru Liu, Yi Yuan, Zhongshang Xue, Fuzhong BMJ Open Epidemiology OBJECTIVES: In observational studies, epidemiologists often attempt to estimate the total effect of an exposure on an outcome of interest. However, when the underlying diagram is unknown and limited knowledge is available, dissecting bias performances is essential to estimating the total effect of an exposure on an outcome when mistakenly adjusting for mediators under logistic regression. Through simulation, we focused on six causal diagrams concerning different roles of mediators. Sensitivity analysis was conducted to assess the bias performances of varying across exposure-mediator effects and mediator-outcome effects when adjusting for the mediator. SETTING: Based on the causal relationships in the real world, we compared the biases of varying across the effects of exposure-mediator with those of varying across the effects of mediator-outcome when adjusting for the mediator. The magnitude of the bias was defined by the difference between the estimated effect (using logistic regression) and the total effect of the exposure on the outcome. RESULTS: In four scenarios (a single mediator, two series mediators, two independent parallel mediators or two correlated parallel mediators), the biases of varying across the effects of exposure-mediator were greater than those of varying across the effects of mediator-outcome when adjusting for the mediator. In contrast, in two other scenarios (a single mediator or two independent parallel mediators in the presence of unobserved confounders), the biases of varying across the effects of exposure-mediator were less than those of varying across the effects of mediator-outcome when adjusting for the mediator. CONCLUSIONS: The biases were more sensitive to the variation of effects of exposure-mediator than the effects of mediator-outcome when adjusting for the mediator in the absence of unobserved confounders, while the biases were more sensitive to the variation of effects of mediator-outcome than those of exposure-mediator in the presence of an unobserved confounder. BMJ Publishing Group 2017-11-20 /pmc/articles/PMC5719285/ /pubmed/29162569 http://dx.doi.org/10.1136/bmjopen-2016-015640 Text en © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2017. All rights reserved. No commercial use is permitted unless otherwise expressly granted. This is an Open Access article distributed in accordance with the terms of the Creative Commons Attribution (CC BY 4.0) license, which permits others to distribute, remix, adapt and build upon this work, for commercial use, provided the original work is properly cited. See: http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Epidemiology Wang, Tingting Li, Hongkai Su, Ping Yu, Yuanyuan Sun, Xiaoru Liu, Yi Yuan, Zhongshang Xue, Fuzhong Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies |
title | Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies |
title_full | Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies |
title_fullStr | Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies |
title_full_unstemmed | Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies |
title_short | Sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies |
title_sort | sensitivity analysis for mistakenly adjusting for mediators in estimating total effect in observational studies |
topic | Epidemiology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719285/ https://www.ncbi.nlm.nih.gov/pubmed/29162569 http://dx.doi.org/10.1136/bmjopen-2016-015640 |
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