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Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis

by: Ulrika Wählby, E. Niclas Jonsson, and Mats O. Karlsson

In: AAPS pharmSci, Vol. 4, Nr. 4 (2002) , p. E27.

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URL:	doi:10.1208/ps040427

The aim of this study was to compare 2 stepwise covariate model-building strategies, frequently used in the analysis of pharmacokinetic-pharmacodynamic PK-PD data using nonlinear mixed-effects models, with respect to included covariates and predictive performance. In addition, the effects of stepwise regression on the estimated covariate coefficients were assessed. Using simulated and real PK data, covariate models were built applying 1 stepwise generalized additive models GAM for identifying potential covariates, followed by backward elimination in the computer program NONMEM, and 2 stepwise forward inclusion and backward elimination in NONMEM. Different versions of these procedures were tried eg, treating different study occasions as separate individuals in the GAM, or fixing a part of the parameters when the NONMEM procedure was used. The final covariate models were compared, including their ability to predict a separate data set or their performance in cross-validation. T

BibTeX record

@article{Wahlby.2002,
  abstract = {The aim of this study was to compare 2 stepwise covariate model-building strategies, frequently used in the analysis of pharmacokinetic-pharmacodynamic (PK-PD) data using nonlinear mixed-effects models, with respect to included covariates and predictive performance. In addition, the effects of stepwise regression on the estimated covariate coefficients were assessed. Using simulated and real PK data, covariate models were built applying (1) stepwise generalized additive models (GAM) for identifying potential covariates, followed by backward elimination in the computer program NONMEM, and (2) stepwise forward inclusion and backward elimination in NONMEM. Different versions of these procedures were tried (eg, treating different study occasions as separate individuals in the GAM, or fixing a part of the parameters when the NONMEM procedure was used). The final covariate models were compared, including their ability to predict a separate data set or their performance in cross-validation. T},
  added-at = {2010-02-04T16:31:48.000+0100},
  author = {Wählby, Ulrika and Jonsson, E. Niclas and Karlsson, Mats O.},
  biburl = {http://www.bibsonomy.org/bibtex/291b3044d82975a6d97f72655ab2a3813/hruehs},
  interhash = {b9dda743c2751f625ac93eea63bb3a10},
  intrahash = {91b3044d82975a6d97f72655ab2a3813},
  issn = {1522-1059},
  journal = {AAPS pharmSci},
  keywords = {Bias Models, Pharmacokinetics Predictive Selection Statistical Tests Value of},
  number = 4,
  pages = {E27},
  timestamp = {2010-02-04T16:31:48.000+0100},
  title = {Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis},
  url = {doi:10.1208/ps040427},
  volume = 4,
  year = 2002
}

Endnote record

%0 Journal Article
%1 Wahlby.2002
%A Wählby, Ulrika
%A Jonsson, E. Niclas
%A Karlsson, Mats O.
%D 2002
%J AAPS pharmSci
%K 
%N 4
%P E27
%T Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis
%U doi:10.1208/ps040427
%V 4
%X The aim of this study was to compare 2 stepwise covariate model-building strategies, frequently used in the analysis of pharmacokinetic-pharmacodynamic (PK-PD) data using nonlinear mixed-effects models, with respect to included covariates and predictive performance. In addition, the effects of stepwise regression on the estimated covariate coefficients were assessed. Using simulated and real PK data, covariate models were built applying (1) stepwise generalized additive models (GAM) for identifying potential covariates, followed by backward elimination in the computer program NONMEM, and (2) stepwise forward inclusion and backward elimination in NONMEM. Different versions of these procedures were tried (eg, treating different study occasions as separate individuals in the GAM, or fixing a part of the parameters when the NONMEM procedure was used). The final covariate models were compared, including their ability to predict a separate data set or their performance in cross-validation. T

BibSonomy :: publication ::

Comparison of stepwise covariate model building strategies in population pharmacokinetic-pharmacodynamic analysis

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Abstract

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Endnote record