Mathematical Modeling of Pharmacokinetic Data Book

Preface
1. Why Model the Data?:
Condense the Data - Exploring Mechanisms - Making Predictions
2. General Approach:
General Method Error in y Alone - Parameter Adjustments
3. Pharmacokinetic Models:
Compartmental Models - Physiologically Based Models - Pharmacodynamic Models
4. Simulation of Data:
Explicit Equations - Implicit Equations - Differential Equations - Integration Using Laplace Transforms - Numerical Integration of Differential Equations
5. Initial Estimates:
Graphical Methods - Linear Regression - Curve Stripping - Area under the Curve Estimation - Deconvolution
6. Non-Linear Regression:
Grid Search Method - Steepest Descent Method - Gauss-Newton Methods - Simplex Method - Local Minima
7. Weighting Schemes:
Equal Weight - Variance Model - Iteratively Reweighted Least Squares - Extended Least Squares - Bayesian Methods - Analysis of Population Data
8. Evaluation of Program Output:
Tabular Output - Graphical Output - Statistical Output
9. Experimental Design:
Pilot Study - Identifiability-Sampling Sites - Optimal Sampling-Sampling Times - Model Testing - Appendix - References - Index