Estimation and inference in pharmacokinetic models: The effectiveness of model reformulation and resampling methods for functions of parameters
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It is well known that high parameter estimate correlations and asymptotic variance estimates can cause estimation and inference problems in the analysis of pharmacokinetic models. In this paper we show that analysis of three important functions of pharmacokinetic parameters, the half-life, mean residence time, and the area under the curve, can sometimes be greatly improved by reformulating the model to address collinearity and by using the bootstrap to form confidence intervals. The resultant estimators can be more accurate than the original ones, and resultant confidence intervals can be narrower. Of the three measures, the half-life estimator is much better behaved than the estimators of mean residence time and area under the curve under collinearity, suggesting that it (or measures like it) should be used more often.
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Journal of Pharmacokinetics and Biopharmaceutics
Volume 18, Issue 4 , pp 361-377
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- Online ISSN
- Kluwer Academic Publishers-Plenum Publishers
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- pharmacokinetic modeling
- nonlinear estimation
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- Author Affiliations
- 1. Division of Biostatistics, Memorial Sloan-Kettering Cancer Center, 1275 York Avenue, 10021, New York, New York
- 2. Department of Statistics and Operations Research, Leonard N. Stern School of Business, New York University, 90 Trinity Place, 10006, New York, New York