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Tob Regul Sci

Title:		Analysis of Multiple Biomarkers Using Structural Equation Modeling
Author(s):		Cao W; Hecht SS; Murphy SE; Chu H; Benowitz NL; Donny EC; Hatsukami DK; Luo X;
Address:		"Wenhao Cao, Master of Science Student, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN. Stephen S. Hecht, Professor, Masonic Cancer Center, University of Minnesota, Minneapolis, MN. Sharon E. Murphy, Professor, Masonic Cancer Center, University of Minnesota, Minneapolis, MN. Haitao Chu, Professor, Division of Biostatistics, School of Public Health, University of Minnesota, Minneapolis, MN. Neal L. Benowitz, Professor, University of California, Department of Medicine, San Francisco, CA. Eric C. Donny, Professor, Wake Forest School of Medicine, Department of Physiology and Pharmacology, Winston-Salem, NC. Dorothy K. Hatsukami, Professor, Masonic Cancer Center and Department of Psychiatry, University of Minnesota, Minneapolis, MN. Xianghua Luo, Associate Professor, Division of Biostatistics School of Public Health and Masonic Cancer Center, University of Minnesota, Minneapolis, MN"
Journal Title:		Tob Regul Sci
Year:		2020
Volume:		6
Issue:		4
Page Number:		266 - 278
DOI:		10.18001/trs.6.4.4
ISSN/ISBN:		2333-9748 (Print) 2333-9748 (Electronic) 2333-9748 (Linking)
Abstract:		"OBJECTIVES: When examining the relationship between smoking intensity and toxicant exposure biomarkers in an effort to understand the potential risk for smoking-related disease, individual biomarkers may not be strongly associated with smoking intensity because of the inherent variability in biomarkers. Structural equation modeling (SEM) offers a powerful solution by modeling the relationship between smoking intensity and multiple biomarkers through a latent variable. METHODS: Baseline data from a randomized trial (N = 1250) were used to estimate the relationship between smoking intensity and a latent toxicant exposure variable summarizing five volatile organic compound biomarkers. Two variables of smoking intensity were analyzed: the self-report cigarettes smoked per day and total nicotine equivalents in urine. SEM was compared with linear regression with each biomarker analyzed individually or with the sum score of the five biomarkers. RESULTS: SEM models showed strong relationships between smoking intensity and the latent toxicant exposure variable, and the relationship was stronger than its counterparts in linear regression with each biomarker analyzed separately or with the sum score. CONCLUSIONS: SEM is a powerful multivariate statistical method for studying multiple biomarkers assessing the same class of harmful constituents. This method could be used to evaluate exposure from different combusted tobacco products"
Keywords:		biological marker (biomarker) cigarette smoke latent variable multivariate statistical method structural equation modelling;
Notes:		"PubMed-not-MEDLINECao, Wenhao Hecht, Stephen S Murphy, Sharon E Chu, Haitao Benowitz, Neal L Donny, Eric C Hatsukami, Dorothy K Luo, Xianghua eng P30 CA077598/CA/NCI NIH HHS/ U54 DA031659/DA/NIDA NIH HHS/ UL1 TR000114/TR/NCATS NIH HHS/ 2020/07/01 Tob Regul Sci. 2020 Jul; 6(4):266-278. doi: 10.18001/trs.6.4.4"