A procedure for generating bootstrap samples for the validation of nonlinear mixed-effects population models.

Area of Science:

• Pharmacometrics
• Computational Biology
• Statistical Modeling

Background:

• Population pharmacokinetic (PopPK) and pharmacodynamic (PopPD) models are crucial for drug development.
• Validating these models, especially with small datasets, requires robust statistical methods like bootstrapping.
• Existing methods for bootstrap sample preparation can be resource-intensive and require specialized software.

Purpose of the Study:

• To present an automated, accessible method for preparing bootstrap data samples for model validation.
• To reduce reliance on expensive statistical software packages for bootstrapping.
• To facilitate the validation of PopPK/PopPD models developed with limited subject data.

Main Methods:

• Utilized an MS-DOS batch file and an AWK script for automated bootstrap data sampling.
• Employed the public-domain text processing program AWK (single EXE file).
• Demonstrated adaptability for UNIX users and other biomedical modeling applications.

Main Results:

• Successfully automated the preparation and presentation of bootstrap samples for NONMEM.
• Provided a cost-effective solution by avoiding high-end statistical packages.
• The method is efficient and can be readily adapted for various bootstrapping needs.

Conclusions:

• The presented automated method simplifies bootstrap data preparation for PopPK/PopPD model validation.
• This approach is a valuable, economical alternative for researchers working with small datasets.
• The technique's flexibility allows for application in diverse biomedical modeling scenarios.