Biostatistics Weekly Seminar

Objective measures of oxygen consumption and carbon dioxide production by mammals are used to predict their energy expenditure. Since energy expenditure is not directly observable, it can be viewed as a latent construct with multiple physical indirect measures such as respiratory quotient, volumetric oxygen consumption and volumetric carbon dioxide production. Metabolic rate is defined as the rate at which metabolism occurs in the body. Metabolic rate is also not directly observable. However, heat is produced as a result of metabolic processes within the body. Therefore, metabolic rate can be approximated by heat production plus some error. While energy expenditure and metabolic rates are correlated, they are not equivalent. Energy expenditure results from physical function while metabolism can occur within the body without the occurrence of physical activities. In this talk, I will present a novel approach for studying the relationship between metabolic rate and indicators of energy expenditure. This will be accomplished by extending our previous work on multiple indicators, multiple causes measurement error (MIMIC ME) models to allow responses that are sparsely observed functional data, defining the sparse functional multiple indicators, multiple cause measurement error (FMIMIC ME) models. The mean curves are modeled using basis splines. A novel approach for estimating the variance of the classical measurement error based on functional principal components will be discussed. The model parameters are estimated using the EM algorithm. An application to data collected on Zucker diabetic fatty rats will be presented for illustration.

MRBIII, Room 1220
12 September 2018
1:30pm