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A novel method for controlling unobserved confounding using double confounders

BACKGROUND: Controlling unobserved confounding still remains a great challenge in observational studies, and a series of strict assumptions of the existing methods usually may be violated in practice. Therefore, it is urgent to put forward a novel method. METHODS: We are interested in the causal eff...

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Autores principales: Liu, Lu, Hou, Lei, Yu, Yuanyuan, Liu, Xinhui, Sun, Xiaoru, Yang, Fan, Wang, Qing, Jing, Ming, Xu, Yeping, Li, Hongkai, Xue, Fuzhong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374896/
https://www.ncbi.nlm.nih.gov/pubmed/32698801
http://dx.doi.org/10.1186/s12874-020-01049-0
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author Liu, Lu
Hou, Lei
Yu, Yuanyuan
Liu, Xinhui
Sun, Xiaoru
Yang, Fan
Wang, Qing
Jing, Ming
Xu, Yeping
Li, Hongkai
Xue, Fuzhong
author_facet Liu, Lu
Hou, Lei
Yu, Yuanyuan
Liu, Xinhui
Sun, Xiaoru
Yang, Fan
Wang, Qing
Jing, Ming
Xu, Yeping
Li, Hongkai
Xue, Fuzhong
author_sort Liu, Lu
collection PubMed
description BACKGROUND: Controlling unobserved confounding still remains a great challenge in observational studies, and a series of strict assumptions of the existing methods usually may be violated in practice. Therefore, it is urgent to put forward a novel method. METHODS: We are interested in the causal effect of an exposure on the outcome, which is always confounded by unobserved confounding. We show that, the causal effect of an exposure on a continuous or categorical outcome is nonparametrically identified through only two independent or correlated available confounders satisfying a non-linear condition on the exposure. Asymptotic theory and variance estimators are developed for each case. We also discuss an extension for more than two binary confounders. RESULTS: The simulations show better estimation performance by our approach in contrast to the traditional regression approach adjusting for observed confounders. A real application is separately applied to assess the effects of Body Mass Index (BMI) on Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Fasting Blood Glucose (FBG), Triglyceride (TG), Total Cholesterol (TC), High Density Lipoprotein (HDL) and Low Density Lipoprotein (LDL) with individuals in Shandong Province, China. Our results suggest that SBP increased 1.60 (95% CI: 0.99–2.93) mmol/L with per 1- kg/m(2) higher BMI and DBP increased 0.37 (95% CI: 0.03–0.76) mmol/L with per 1- kg/m(2) higher BMI. Moreover, 1- kg/m(2) increase in BMI was causally associated with a 1.61 (95% CI: 0.96–2.97) mmol/L increase in TC, a 1.66 (95% CI: 0.91–55.30) mmol/L increase in TG and a 2.01 (95% CI: 1.09–4.31) mmol/L increase in LDL. However, BMI was not causally associated with HDL with effect value − 0.20 (95% CI: − 1.71–1.44). And, the effect value of FBG per 1- kg/m(2) higher BMI was 0.56 (95% CI: − 0.24–2.18). CONCLUSIONS: We propose a novel method to control unobserved confounders through double binary confounders satisfying a non-linear condition on the exposure which is easy to access.
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spelling pubmed-73748962020-07-22 A novel method for controlling unobserved confounding using double confounders Liu, Lu Hou, Lei Yu, Yuanyuan Liu, Xinhui Sun, Xiaoru Yang, Fan Wang, Qing Jing, Ming Xu, Yeping Li, Hongkai Xue, Fuzhong BMC Med Res Methodol Technical Advance BACKGROUND: Controlling unobserved confounding still remains a great challenge in observational studies, and a series of strict assumptions of the existing methods usually may be violated in practice. Therefore, it is urgent to put forward a novel method. METHODS: We are interested in the causal effect of an exposure on the outcome, which is always confounded by unobserved confounding. We show that, the causal effect of an exposure on a continuous or categorical outcome is nonparametrically identified through only two independent or correlated available confounders satisfying a non-linear condition on the exposure. Asymptotic theory and variance estimators are developed for each case. We also discuss an extension for more than two binary confounders. RESULTS: The simulations show better estimation performance by our approach in contrast to the traditional regression approach adjusting for observed confounders. A real application is separately applied to assess the effects of Body Mass Index (BMI) on Systolic Blood Pressure (SBP), Diastolic Blood Pressure (DBP), Fasting Blood Glucose (FBG), Triglyceride (TG), Total Cholesterol (TC), High Density Lipoprotein (HDL) and Low Density Lipoprotein (LDL) with individuals in Shandong Province, China. Our results suggest that SBP increased 1.60 (95% CI: 0.99–2.93) mmol/L with per 1- kg/m(2) higher BMI and DBP increased 0.37 (95% CI: 0.03–0.76) mmol/L with per 1- kg/m(2) higher BMI. Moreover, 1- kg/m(2) increase in BMI was causally associated with a 1.61 (95% CI: 0.96–2.97) mmol/L increase in TC, a 1.66 (95% CI: 0.91–55.30) mmol/L increase in TG and a 2.01 (95% CI: 1.09–4.31) mmol/L increase in LDL. However, BMI was not causally associated with HDL with effect value − 0.20 (95% CI: − 1.71–1.44). And, the effect value of FBG per 1- kg/m(2) higher BMI was 0.56 (95% CI: − 0.24–2.18). CONCLUSIONS: We propose a novel method to control unobserved confounders through double binary confounders satisfying a non-linear condition on the exposure which is easy to access. BioMed Central 2020-07-22 /pmc/articles/PMC7374896/ /pubmed/32698801 http://dx.doi.org/10.1186/s12874-020-01049-0 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data.
spellingShingle Technical Advance
Liu, Lu
Hou, Lei
Yu, Yuanyuan
Liu, Xinhui
Sun, Xiaoru
Yang, Fan
Wang, Qing
Jing, Ming
Xu, Yeping
Li, Hongkai
Xue, Fuzhong
A novel method for controlling unobserved confounding using double confounders
title A novel method for controlling unobserved confounding using double confounders
title_full A novel method for controlling unobserved confounding using double confounders
title_fullStr A novel method for controlling unobserved confounding using double confounders
title_full_unstemmed A novel method for controlling unobserved confounding using double confounders
title_short A novel method for controlling unobserved confounding using double confounders
title_sort novel method for controlling unobserved confounding using double confounders
topic Technical Advance
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7374896/
https://www.ncbi.nlm.nih.gov/pubmed/32698801
http://dx.doi.org/10.1186/s12874-020-01049-0
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