Lesson Ten:

Data Analysis & Regression

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In this lesson we will explore data analysis issues in pharmacokinetics. In the recorded presentations we talk about the philosophy of data analysis in pharmacokinetics and pharmacodynamics and then review linear regression. Finally we describe the differences between linear regression and nonlinear regression and give a brief introduction to maximum likelihood estimation, which is the main method used in nonlinear mixed effects modelling.


You will also find a manuscript by Boxenbaum et al. This manuscript was meant to be a tutorial on pharmacokinetic data analysis and although it was published some time ago it is a useful introduction to many of the issues in nonlinear regression as applied to pharmacokinetics.

Learning objectives:

  • After this presentations you should have a clearer understanding of pharmacokinetic data analysis

  • You should understand how to select an appropriate model given a particular data set and dosing information

  • You should be able to diagnose model fit and perform simple statistical evaluation of the fit



1.1 Please study the video presentations below:

Please study the 4 recorded Powerpoint lecture presentations below:

You can download a PDF of the presentation files here:

Data Analysis 1
Data Analysis 2
Data Analysis 3
Data Analysis 4

In addition, study the Boxenbaum manuscript below:

Boxenbaum Paper


You can view a pre-recorded tutorial of this lesson here. [ Video ]

Additional Resources:

  • N.R.Draper, H.Smith, Applied Regression Analysis, Wiley, 1998

  • Y. Bard , Nonlinear Parameter Estimation, Academic Press, 1974

  • P.L. Bonate, ‘Pharmacokinetic-Pharmacodynamic Modelling and Simulation’, Springer, 2006

  • M. Davidian, D.M. Giltinan, ‘Nonlinear models for repeated measures data’, Chapman & Hall, 1995