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Journal of Pharmacokinetics and Biopharmaceutics

, Volume 17, Issue 5, pp 601–614 | Cite as

Fitting heteroscedastic regression models to individual pharmacokinetic data using standard statistical software

  • David M. Giltinan
  • David Ruppert
Pharmacometrics

Abstract

In the analysis of individual pharmacokinetic data by nonlinear regression it is important to allow for possible heterogeneity of variance in the response. Two common methods of doing this are weighted least squares with appropriate weights or data transformation using a suitable transform. With either approach it is appealing to let the data determine the appropriate choice of weighting scheme or transformation. This article describes two methods of doing this which are easy to compute using standard statistical software. The first method is a generalized least squares scheme for the case where the variance is assumed proportional to an unknown power of the mean. The second involves applying a power transformation to both sides of the regression equation. It is shown that both techniques may be implemented using only nonlinear regression routines. Sample code is provided for their implementation using the SAS software package. However, the proposed methods are feasible using any software package that incorporates a nonlinear least squares routine, and are thus well suited to routine use.

Key words

generalized least squares weighted least squares transform both sides power transformations heteroscedasticity nonlinear regression 

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Copyright information

© Plenum Publishing Corporation 1989

Authors and Affiliations

  • David M. Giltinan
    • 1
  • David Ruppert
    • 2
  1. 1.Medical Research DivisionAmerican Cyanamid CompanyPearl River
  2. 2.School of Operations Research and Industrial EngineeringCornell UniversityIthaca

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