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Journal of Pharmacokinetics and Biopharmaceutics

, Volume 14, Issue 5, pp 539–555 | Cite as

Analysis of pharmacokinetic data using parametric models. III. Hypothesis tests and confidence intervals

  • Lewis B. Sheiner
Tutorial Section

Abstract

This is the third in a series of tutorial articles discussing the analysis of pharmacokinetic data using parametric models. In this article the concern is how to test hypotheses about, and assign confidence intervals to, the values of the parameters of such models. The basic approach to both tasks involves determining the goodness of fit of the model to the data for alternative values of the parameters and using the change in goodness of fit to assess the plausibility of the alternative values. The goodness of fit is measured by the value of a (least-squares-type) objective function. An approximation to the dependence of the latter on the parameter values yields an estimate of the familiar asymptotic covariance matrix of the estimates. The latter can also be used to test hypotheses about, and assign confidence intervals to, functions of parameters.

Key words

models data analysis regression least squares confidence intervals precision hypothesis tests 

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Copyright information

© Plenum Publishing Corporation 1986

Authors and Affiliations

  • Lewis B. Sheiner
    • 1
    • 2
  1. 1.Department of Laboratory Medicine, School of MedicineUniversity of CaliforniaSan Francisco
  2. 2.Division of Clinical Pharmacology, Departments of Medicine and Pharmacy, Schools of Medicine and PharmacyUniversity of CaliforniaSan Francisco

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