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 (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) 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).
来自章节 7:
Now Playing
Pharmacokinetic Models
23 Views
Pharmacokinetic Models
80 Views
Pharmacokinetic Models
70 Views
Pharmacokinetic Models
144 Views
Pharmacokinetic Models
165 Views
Pharmacokinetic Models
54 Views
Pharmacokinetic Models
140 Views
Pharmacokinetic Models
50 Views
Pharmacokinetic Models
186 Views
Pharmacokinetic Models
327 Views
Pharmacokinetic Models
119 Views
Pharmacokinetic Models
79 Views
Pharmacokinetic Models
86 Views
Pharmacokinetic Models
351 Views
Pharmacokinetic Models
186 Views
See More
版权所属 © 2025 MyJoVE 公司版权所有,本公司不涉及任何医疗业务和医疗服务。