Abstract
We propose a new method for optimal experimental design of population pharmacometric experiments based on global search methods using interval analysis; all variables and parameters are represented as intervals rather than real numbers. The evaluation of a specific design is based on multiple simulations and parameter estimations. The method requires no prior point estimates for the parameters, since the parameters can incorporate any level of uncertainty. In this respect, it is similar to robust optimal design. Representing sampling times and covariates like doses by intervals gives a direct way of optimizing with rigorous sampling and dose intervals that can be useful in clinical practice. Furthermore, the method works on underdetermined problems for which traditional methods typically fail.
Original language | English |
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Pages (from-to) | 495-507 |
Number of pages | 13 |
Journal | The AAPS Journal |
Volume | 13 |
Issue number | 4 |
DOIs | |
Publication status | Published - 1 Dec 2011 |
Externally published | Yes |
Keywords
- interval analysis
- optimal experimental design
- set-values methods