Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (X_i), observed concentrations (C_i), measurement errors (ε_i), model parameters (ϕ_j), and the related function (ƒ_i) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS) method favors better predictions for larger observations. In contrast, weighted least squares (WLS) and maximum likelihood/expected least squares (ML/ELS) methods improve OLS by incorporating a weighting factor.

Population analysis models predict concentration data for multiple individuals, accounting for interindividual variability and providing individual and population predictions. The same structural model fits all individuals' data for a specific drug under study. Different types of population compartmental analysis include naïve-average data, naïve pooled data, and the two-stage approach, which includes standard, global, and iterative types. In the two-stage approach, population parameter estimates are obtained through iterative processes, such as standard two-stage (STS) and global two-stage (GTS).