Abstract
This is the first in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article, the purposes of modelling are discussed; regression models for individuals and populations are defined; and structural and variance models are discussed as the two required submodels of the overall regression model. Topics of future articles are: point estimates of parameters; interval estimates of parameters; model criticism and choosing among contending models; population kinetic models and estimation; and elements of optimal design.
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References
S. Wold, Spline functions in data analyses.Technometrics 16, 1–11 (1974).
K. C. Yeh and K. C. Kwan. A comparison of numerical integrating algorithms by trapezoidal, Lagrange, and spline approximation.J. Pharmakokin. Biopharm. 6:79–98 (1978).
G. E. P. Box and W. J. Hill. Correcting inhomogeneity of variance with power transformation weighting.Technometrics 16:385–389 (1974).
D. J. Finney and P. Phillips. The form and estimation of a variance function with particular reference to radioimmuno-assays.Appl. Stat. 26:312–320 (1977).
G. M. Raab, Estimation of a variance function, with application to immunoassay.Appl. Stat. 30:32–40 (1981).
L. Endrenyi.Kinetic Data Analysis. Plenum Press, New York, 1981.
S. L. Beal and L. B. Sheiner. Estimating population kinetics.CRC Crit. Rev. Bioeng. 8:195–222 (1982).
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Work supported in part by NIH Grant GM 26676, and GM 26691.
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Sheiner, L.B. Analysis of pharmacokinetic data using parametric models—1: Regression models. Journal of Pharmacokinetics and Biopharmaceutics 12, 93–117 (1984). https://doi.org/10.1007/BF01063613
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DOI: https://doi.org/10.1007/BF01063613