Read more
A concise guide to mathematical modeling and analysis of pharmacokinetic data, this book contains valuable methods for maximizing the information obtained from given data. It is an ideal resource for scientists, scholars, and advanced students.
List of contents
Why Model the Data? Condense the Data. Exploring Mechanisms. Making Predictions. General Approach. General Method Error in y Alone. Parameter Adjustments. Pharmacokinetic Models. Compartmental Models. Physiologically Based Models. Pharmacodynamic Models. Simulation of Data. Explicit Equations. Implicit Equations. Differential Equations. Integration Using Laplace Transforms. Numerical Integration of Differential Equations. Initial Estimates. Graphical Methods. Linear Regression. Curve Stripping. Area under the Curve Estimation. Deconvolution. Non-Linear Regression. Grid Search Method. Steepest Descent Method. Gauss-Newton Methods. Simplex Method. Local Minima. Weighting Schemes. Equal Weight. Variance Model. Iteratively Reweighted Least Squares. Extended Least Squares. Bayesian Methods. Analysis of Population Data. Evaluation of Program Output. Tabular Output. Graphical Output. Statistical Output. Experimental Design. Pilot Study. Identifiability-Sampling Sites. Optimal Sampling-Sampling Times. Model Testing. Appendix. References. Index.
About the author
Bourne, DavidW.A.
Summary
A guide to mathematical modeling and analysis of pharmacokinetic data. It covers methods for maximizing the information obtained from given data. It presents an approach to using the mathematical software tools available so that the investigator may extract the maximum information from a given set of data.
