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Performance Modelling of Cardiovascular System of Linear Mixed Models
by
Anil Kumar
Department of Mathematics , Dr KN Modi Institute of Engg. & Tech. , Modinagar, UP India
In this paper a statistical approaches in cardiovascular research has based on variance analysis (ANOVA). However, most of the time, the assumption that data are independent is violated since several measures are performed on the same subject (repeated measures). In addition, the presence of intra- and inter-observers variability can potentially obscure significant differences. The linear mixed model (LMM) is an extended multivariate linear regression method of analysis that accounts for both fixed and random effects. Linear mixed model allows for addressing incomplete design cases, leading to more efficient parameter estimates. In present study, linear mixed model was applied to two sets of cardiovascular research data and compared to ANOVA. The first example is an analysis of heart rate in mice after atropine and propranolol injections. Linear mixed model shows an important mouse random effects that depends on pharmacological treatment and provides with accurate estimates for each significant experimental factors. When randomly suppressing observations from the data sets (20-30%) the time factor of Anova model becomes non significant while linear mixed model still remains significant. The second example is the analysis of isolated coronary-perfused pressure of transgenic mice hearts. Linear mixed model evidenced a significant transgenic effect in both male and female animals, while, with ANOVA, the transgenic effects have limited to male mice only.
Date received: December 7, 2006
Copyright © 2006 by the author(s). The author(s) of this document and the organizers of the conference have granted their consent to include this abstract in Atlas Conferences Inc. Document # cata-20.