Maiying Kong and Hyejeong Jang
University of Louisville
Mixed-Effects Models for Modeling Cardiac Functions and Testing Treatment Effects
Mixed- effects model is an efficient tool for analyzing longitudinal data. The random effects in mixed models can be used to capture the correlations between repeated measurements within a subject. The time points are not fixed and all available data can be used in mixed-effects model, provided data are missing at random. For this reason, we focus on applying mixed-effects models to the repeated measurements of different aspects of cardiac functions, such as heart rate, the left ventricle developed blood pressure, and coronary blood flow, in the Gluatathione-S-transferase (GSTP) gene knockout and wild-type mice experienced iscchemia/reperfusion injury. Each aspect of the cardiac function consists of measurements from three time periods: preischemic, ischemic, and reperfusion periods. We develop piecewise nonlinear function to describe the different aspects of the cardiac function. We apply nonlinear mixed effects models and changing point model to examine the cardiac functions experienced iscchemia/reperfusion injury and to compare group differences.