Mixed-effects models for modeling cardiac functions and treatment effects.

TL;DRAbstract

Mixed-effects model is an efficient tool for analyzing longitudinal data. The random effects in mixed-effects model can be used to capture the correlations among 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 cardiac function including heart rate, left ventricle developed pressure, and coronary flow in the Glutathione-S-transferase P (GSTP) gene knockout and wild-type mice following ischemial/reperfusion injury performed in the isolated, Langendorff-perfused heart. Cardiac function is measured during three time periods: pre-ischemia, ischemia (no flow), and reperfusion. We developed piecewise nonlinear mixed-effects model to describe the different aspects of the cardiac function during each period. We applied nonlinear mixed-effects models and a changing point model to examine how

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