nonmem proportional additive error model

Population pharmacokinetics does not require “rich” data (many observations/subject), as required for analysis of single-subject data, nor is there a need for structured sampling time schedules. Other goals include relating observed concentrations to administered doses through identification of predictive covariates in a target population. 1Ī primary goal of most population pharmacokinetic modeling evaluations is finding population pharmacokinetic parameters and sources of variability in a population. Nonlinear mixed effects modeling software brings data and models together, implementing an estimation method for finding parameters for the structural, statistical, and covariate models that describe the data. Covariate models explain variability predicted by subject characteristics (covariates). Statistical models account for “unexplainable” (random) variability in concentration within the population (e.g., between-subject, between-occasion, residual, etc.). Structural models describe the typical concentration time course within the population. “Mixed-effects” refers to the parameterization: parameters that do not vary across individuals are referred to as “fixed effects,” parameters that vary across individuals are called “random effects.” There are five major aspects to developing a population pharmacokinetic model: (i) data, (ii) structural model, (iii) statistical model, (iv) covariate models, and (v) modeling software.

“Nonlinear” refers to the fact that the dependent variable (e.g., concentration) is nonlinearly related to the model parameters and independent variable(s). Population pharmacokinetics is the study of pharmacokinetics at the population level, in which data from all individuals in a population are evaluated simultaneously using a nonlinear mixed-effects model.

Nonmem proportional additive error model