Influence of Erroneous Patient Records on Population Pharmacokinetic Modeling and Individual Bayesian Estimation

Aize Franciscus van der Meer*, Daniel J. Touw, Marco A. E. Marcus, Cornelis Neef, Johannes H. Proost

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Web of Science)

Abstract

Background: Observational data sets can be used for population pharmacokinetic (PK) modeling. However, these data sets are generally less precisely recorded than experimental data sets. This article aims to investigate the influence of erroneous records on population PK modeling and individual maximum a posteriori Bayesian (MAPB) estimation. Methods: A total of 1123 patient records of neonates who were administered vancomycin were used for population PK modeling by iterative 2-stage Bayesian (ITSB) analysis. Cut-off values for weighted residuals were tested for exclusion of records from the analysis. A simulation study was performed to assess the influence of erroneous records on population modeling and individual MAPB estimation. Also the cut-off values for weighted residuals were tested in the simulation study. Results: Errors in registration have limited the influence on outcomes of population PK modeling but can have detrimental effects on individual MAPB estimation. A population PK model created from a data set with many registration errors has little influence on subsequent MAPB estimates for precisely recorded data. A weighted residual value of 2 for concentration measurements has good discriminative power for identification of erroneous records. Conclusions: ITSB analysis and its individual estimates are hardly affected by most registration errors. Large registration errors can be detected by weighted residuals of concentration.
Original languageEnglish
Pages (from-to)526-534
JournalTherapeutic Drug Monitoring
Volume34
Issue number5
DOIs
Publication statusPublished - Oct 2012

Keywords

  • maximum a posteriori Bayesian estimation
  • pharmacokinetics
  • population modeling
  • patient records
  • error identification

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