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.
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.
Authors: Pieter J Colin; Karel Allegaert; Alison H Thomson; Daan J Touw; Michael Dolton; Matthijs de Hoog; Jason A Roberts; Eyob D Adane; Masato Yamamoto; Dolores Santos-Buelga; Ana Martín-Suarez; Nicolas Simon; Fabio S Taccone; Yoke-Lin Lo; Emilia Barcia; Michel M R F Struys; Douglas J Eleveld Journal: Clin Pharmacokinet Date: 2019-06 Impact factor: 6.447
Authors: Alper Daskapan; Desie Dijkema; Dorien A de Weerd; Wouter F W Bierman; Jos G W Kosterink; Tjip S van der Werf; Jan-Willem C Alffenaar; Ymkje Stienstra Journal: Br J Clin Pharmacol Date: 2017-08-01 Impact factor: 4.335
Authors: Adrin Dadkhah; Dzenefa Alihodzic; Astrid Broeker; Nicolaus Kröger; Claudia Langebrake; Sebastian G Wicha Journal: Pharm Res Date: 2021-10-18 Impact factor: 4.200
Authors: Dzenefa Alihodzic; Astrid Broeker; Michael Baehr; Stefan Kluge; Claudia Langebrake; Sebastian Georg Wicha Journal: Front Pharmacol Date: 2020-03-03 Impact factor: 5.810