%0 Journal Article %1 Wahlby.2004 %A Wählby, Ulrika %A Thomson, Alison H. %A Milligan, Peter A. %A Karlsson, Mats O. %D 2004 %J British Journal of Clinical Pharmacology %K Analysis Chemical Factors Gentamicinspharmacokineticspharmacology Humans Models, Pefloxacinpharmacokineticspharmacology Pharmacokinetics Pharmacology Pyrimidinespharmacokineticspharmacology Regression Time Triazolespharmacokineticspharmacology %N 4 %P 367-377 %T Models for time-varying covariates in population pharmacokinetic-pharmacodynamic analysis %V 58 %X AIM: If appropriately accounted for in a pharmacokinetic (PK)-pharmacodynamic (PD) model, time-varying covariates can provide additional information to that obtained from time-constant covariates. The aim was to present and apply two models applicable to time-varying covariates that capture such additional information. METHODS: The first model estimates different covariate-parameter relationships for within- and between-individual variation in covariate values, by splitting the standard covariate model into a baseline covariate (BCOV) effect and a difference from baseline covariate (DCOV) effect. The second model allows the magnitude of the covariate effect to vary between individuals, by inclusion of interindividual variability in the covariate effect. The models were applied to four previously analysed data sets. RESULTS: The models were applied to 10 covariate-parameter relationships and for three of these the first extended model resulted in a significant improvement of the fit. Ev

@article{Wahlby.2004, abstract = {AIM: If appropriately accounted for in a pharmacokinetic (PK)-pharmacodynamic (PD) model, time-varying covariates can provide additional information to that obtained from time-constant covariates. The aim was to present and apply two models applicable to time-varying covariates that capture such additional information. METHODS: The first model estimates different covariate-parameter relationships for within- and between-individual variation in covariate values, by splitting the standard covariate model into a baseline covariate (BCOV) effect and a difference from baseline covariate (DCOV) effect. The second model allows the magnitude of the covariate effect to vary between individuals, by inclusion of interindividual variability in the covariate effect. The models were applied to four previously analysed data sets. RESULTS: The models were applied to 10 covariate-parameter relationships and for three of these the first extended model resulted in a significant improvement of the fit. Ev}, added-at = {2010-02-04T16:31:48.000+0100}, author = {Wählby, Ulrika and Thomson, Alison H. and Milligan, Peter A. and Karlsson, Mats O.}, biburl = {https://www.bibsonomy.org/bibtex/20ecef74954a02ecff6a6f127c77f7dd4/hruehs}, interhash = {d8b8cde953bc1a7ccf7418ca57c174de}, intrahash = {0ecef74954a02ecff6a6f127c77f7dd4}, issn = {1365-2125}, journal = {British Journal of Clinical Pharmacology}, keywords = {Analysis Chemical Factors Gentamicinspharmacokineticspharmacology Humans Models, Pefloxacinpharmacokineticspharmacology Pharmacokinetics Pharmacology Pyrimidinespharmacokineticspharmacology Regression Time Triazolespharmacokineticspharmacology}, number = 4, pages = {367-377}, timestamp = {2010-02-04T16:31:51.000+0100}, title = {Models for time-varying covariates in population pharmacokinetic-pharmacodynamic analysis}, volume = 58, year = 2004 }