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

Abstract

A MODEL TO PREDICT 24-HOUR URINARY CREATININE LEVEL USING REPEATED MEASUREMENTS By Donna S. Kroos A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science at Virginia Commonwealth University. Virginia Commonwealth University, 2006 Thesis Director: Dr. James E. Mays, Associate Professor, Department of Statistical Sciences & Operations Research 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. X