A Model to Predict 24-Hour Urinary Creatinine Level Using Repeated Measurements

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.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Mathematical Sciences

Year dc:date.available

2006

Author dc:creator

• Kroos, Donna S.

Contributors dc:contributor

• Dr. James E. Mays

• predictive model
• urine
• health
• pesticide
• diet
• Physical Sciences and Mathematics

Statement dc:rights

• © The Author

Identifier

https://scholarscompass.vcu.edu/etd/1172

OAI identifier oai:identifier

oai:scholarscompass.vcu.edu:etd-2171