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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Analysis of Multiple Biomarkers Using Structural Equation Modeling.

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  • 1Wenhao 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.

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Structural equation modeling (SEM) effectively links smoking intensity to toxicant exposure biomarkers, outperforming traditional methods. This approach enhances understanding of smoking-related disease risks by accounting for biomarker variability.

Keywords:
biological marker (biomarker)cigarette smokelatent variablemultivariate statistical methodstructural equation modelling

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Area of Science:

  • Toxicology
  • Biomarkers
  • Statistical Modeling

Background:

  • Individual toxicant exposure biomarkers may show weak associations with smoking intensity due to inherent variability.
  • Understanding smoking intensity's impact on toxicant exposure is crucial for assessing smoking-related disease risks.

Purpose of the Study:

  • To evaluate the relationship between smoking intensity and toxicant exposure using structural equation modeling (SEM).
  • To compare SEM with linear regression methods for analyzing multiple toxicant biomarkers.

Main Methods:

  • Utilized baseline data from a randomized trial (N=1250).
  • Employed SEM to model smoking intensity against a latent toxicant exposure variable summarizing five volatile organic compound biomarkers.
  • Compared SEM with linear regression using individual biomarkers or a sum score.

Main Results:

  • SEM demonstrated strong associations between smoking intensity and the latent toxicant exposure variable.
  • The relationships identified by SEM were stronger than those found using linear regression with individual biomarkers or a sum score.

Conclusions:

  • SEM is a powerful multivariate statistical method for analyzing multiple biomarkers of harmful constituents.
  • This SEM approach can be applied to assess toxicant exposure from various combusted tobacco products.