DOI
https://doi.org/10.25772/TYGE-NB44
Defense Date
2006
Document Type
Thesis
Degree Name
Master of Science
Department
Mathematical Sciences
First Advisor
Dr. James E. Mays
Abstract
Creatinine is a metabolic waste product, removed from the blood by the kidneys, and excreted in the urine. The measurement of creatinine is used in the assessment and monitoring of many medical conditions as well as in the determination or adjustment of absorbed dosage of pesticides. Earlier models to predict 24-hour urinary creatinine used ordinary least squares regression and assumed that the subjects' observations were uncorrelated. However, many of these studies had repeated creatinine measurements for each of their subjects. Repeated measures on the same subject frequently are correlated. Using data from the NIOSH-CDC "Pesticide Dose Monitoring in Turf Applicators" study, this thesis project built a model to predict 24-hour urinary creatinine using the Mixed Model methodology. A covariance structure, that permitted multiple observations for any one individual to be correlated, was identified and utilized. The predictive capabilities of this model were then compared to the earlier models investigated.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
June 2008