Data sets involving nonlinear, sparse grouped data are common in the health sciences, especially in drug trials, where they are used to measure drug absorption, distribution, metabolism, and elimination. In this approach, patients are grouped using characteristics such as age, sex, weight, and smoking history. Given the expense of drug trials, however, it is not always possible to obtain sufficient patient data.
Nonlinear mixed-effects (NLME) modeling provides a good solution for modeling sparse datasets. These models account for both fixed effects (population parameters assumed to be constant each time data is collected) and random effects (sample-dependent random variables). In modeling, random effects act like additional error terms, and their distributions and covariances must be specified. Mixed-effects models provide a reasonable compromise between ignoring data groupings entirely, thereby losing valuable information, and fitting each group with a separate model, which requires significantly larger sample sizes.
Using population pharmacokinetics (popPK) data as an example, this article demonstrates a workflow for implementing a nonlinear mixed-effects model using SimBiology™.
By Kristen Zannella, The MathWorks
This article was published in MATLAB Digest, July 2009.