‎Nonlinear ‌‎R‎egression Mixed Effects Models with Normal‎/ Independent Distribution

A popular application of nonlinear models with mixed effects pharmacokinetic studies‎, ‎in which the distribution of used drug during the life of the individual study‎. ‎The fit of these models assume normality of the random effects and errors are common‎, ‎but can not make it invalid results in the estimation‎. ‎In longitudinal data analysis‎, ‎typically assume that the random effects and random errors are normally distributed‎, ‎but there is a possible violation of empirical studies‎. ‎For this reason‎, ‎the analysis of the pharmacokinetic data such as normal distribution‎, ‎slashe‎, ‎t‎ - ‎student and Contaminated normal considered to be based on analytical achieved‎. ‎In this paper‎, ‎parameter estimation of nonlinear models with mixed effects on the maximum likelihood estimation method and the Bayesian approach respectively by SAS software and Open Bugs pharmacokinetic data set for being carried out‎. ‎Also‎, ‎using the model selection criteria are based on these two approaches‎, ‎we found the best fit model to the data‎.