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Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies

OBJECTIVE: To investigate the importance of controlling confounding factors during binary logistic regression analysis. METHODS: Male coronary heart disease (CHD) patients (n = 664) and healthy control subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric acid, cholesterol, tr...

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Autores principales: Xu, Wenbo, Zhao, Yang, Nian, Shiyan, Feng, Lei, Bai, Xuejing, Luo, Xuan, Luo, Feng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136018/
https://www.ncbi.nlm.nih.gov/pubmed/29882459
http://dx.doi.org/10.1177/0300060518777173
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author Xu, Wenbo
Zhao, Yang
Nian, Shiyan
Feng, Lei
Bai, Xuejing
Luo, Xuan
Luo, Feng
author_facet Xu, Wenbo
Zhao, Yang
Nian, Shiyan
Feng, Lei
Bai, Xuejing
Luo, Xuan
Luo, Feng
author_sort Xu, Wenbo
collection PubMed
description OBJECTIVE: To investigate the importance of controlling confounding factors during binary logistic regression analysis. METHODS: Male coronary heart disease (CHD) patients (n = 664) and healthy control subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric acid, cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100, lipoprotein a, homocysteine, total bilirubin, direct bilirubin, indirect bilirubin, and γ-glutamyl transferase. Associations between these indexes and CHD were assessed by logistic regression, and results were compared by using different analysis strategies. RESULTS: 1) Without controlling for confounding factors, 14 indexes were directly inputted in the analysis process, and 11 indexes were finally retained. A model was obtained with conflicting results. 2) According to the application conditions for logistic regression analysis, all 14 indexes were weighed according to their variances and the results of correlation analysis. Seven indexes were finally included in the model. The model was verified by receiver operating characteristic curve, with an area under the curve of 0.927. CONCLUSIONS: When binary logistic regression analysis is used to evaluate the complex relationships between risk factors and CHD, strict control of confounding factors can improve the reliability and validity of the analysis.
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spelling pubmed-61360182018-09-17 Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies Xu, Wenbo Zhao, Yang Nian, Shiyan Feng, Lei Bai, Xuejing Luo, Xuan Luo, Feng J Int Med Res Clinical Research Reports OBJECTIVE: To investigate the importance of controlling confounding factors during binary logistic regression analysis. METHODS: Male coronary heart disease (CHD) patients (n = 664) and healthy control subjects (n = 400) were enrolled. Fourteen indexes were collected: age, uric acid, cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, apolipoprotein A1, apolipoprotein B100, lipoprotein a, homocysteine, total bilirubin, direct bilirubin, indirect bilirubin, and γ-glutamyl transferase. Associations between these indexes and CHD were assessed by logistic regression, and results were compared by using different analysis strategies. RESULTS: 1) Without controlling for confounding factors, 14 indexes were directly inputted in the analysis process, and 11 indexes were finally retained. A model was obtained with conflicting results. 2) According to the application conditions for logistic regression analysis, all 14 indexes were weighed according to their variances and the results of correlation analysis. Seven indexes were finally included in the model. The model was verified by receiver operating characteristic curve, with an area under the curve of 0.927. CONCLUSIONS: When binary logistic regression analysis is used to evaluate the complex relationships between risk factors and CHD, strict control of confounding factors can improve the reliability and validity of the analysis. SAGE Publications 2018-06-08 2018-09 /pmc/articles/PMC6136018/ /pubmed/29882459 http://dx.doi.org/10.1177/0300060518777173 Text en © The Author(s) 2018 http://creativecommons.org/licenses/by-nc/4.0/ Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Clinical Research Reports
Xu, Wenbo
Zhao, Yang
Nian, Shiyan
Feng, Lei
Bai, Xuejing
Luo, Xuan
Luo, Feng
Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies
title Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies
title_full Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies
title_fullStr Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies
title_full_unstemmed Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies
title_short Differential analysis of disease risk assessment using binary logistic regression with different analysis strategies
title_sort differential analysis of disease risk assessment using binary logistic regression with different analysis strategies
topic Clinical Research Reports
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6136018/
https://www.ncbi.nlm.nih.gov/pubmed/29882459
http://dx.doi.org/10.1177/0300060518777173
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