Document Detail


Fitting nonlinear regression models with correlated errors to individual pharmacodynamic data using SAS software.
MedLine Citation:
PMID:  8576846     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Nonlinear regression is widely used in pharmacokinetic and pharmacodynamic modeling by applying nonlinear ordinary least squares. Although the assumption of independent errors is frequently not fulfilled, this has received scant attention in the pharmacokinetic literature. As in linear regression, leaving correlation of errors out of account leads to an underestimation of the standard deviations of parameter estimates. On the other hand, the use of models that accommodate correlated errors requires more care and more computation. This paper describes a method to fit log-normal functions to individual response curves containing correlated errors by means of statistical software for time series. A sample computer program is given in which the SAS/ETS procedure MODEL is used. In particular, the problem of finding appropriate starting values for nonlinear iterative algorithms is considered. A linear weighted least squares approach for initial parameter estimation is developed. The adequacy of the method is investigated by means of Monte Carlo simulations. Furthermore, the statistical properties of nonlinear least squares with and without accommodating correlated errors are compared. Time action profiles of a long-acting insulin preparation injected subcutaneously in humans are analyzed to illustrate the usefulness of the method proposed.
Authors:
R Bender; L Heinemann
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Publication Detail:
Type:  Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Journal of pharmacokinetics and biopharmaceutics     Volume:  23     ISSN:  0090-466X     ISO Abbreviation:  J Pharmacokinet Biopharm     Publication Date:  1995 Feb 
Date Detail:
Created Date:  1996-03-11     Completed Date:  1996-03-11     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0357115     Medline TA:  J Pharmacokinet Biopharm     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  87-100     Citation Subset:  IM    
Affiliation:
Heinrich-Heine-University Düsseldorf, Department of Metabolic Diseases and Nutrition, Germany.
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MeSH Terms
Descriptor/Qualifier:
Dose-Response Relationship, Drug
Humans
Insulin / pharmacology
Regression Analysis*
Software
Chemical
Reg. No./Substance:
11061-68-0/Insulin

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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