Practical recommendations for population PK studies with sampling time errors.
Purpose: Population pharmacokinetic (PK) data collected from routine clinical practice offers a rich source of valuable information. However, in observational population PK data, accurate time information for blood samples is often missing, resulting in measurement errors (ME) in the sampling time v...
| Published in: | European Journal of Clinical Pharmacology Vol. 69; no. 12; pp. 2055 - 2065 |
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| Main Authors: | , , |
| Format: | equations & formulas research tables/charts Journal Article |
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Springer Nature
Dec2013
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104160783&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104160783 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00316970 NP9 jtl: European Journal of Clinical Pharmacology issn: 00316970 maglogo: N pubinfo: dt: Dec2013 vid: 69 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104160783 92014331 10.1007/s00228-013-1576-7 NLM23975237 104160783 ppf: 2055 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Practical recommendations for population PK studies with sampling time errors. aug: au: Choi, Leena Crainiceanu, Ciprian M. Caffo, Brian S. affil: Department of Biostatistics, School of Medicine, Vanderbilt University, 1161 21st Avenue South, Medical Center North, S-2323, Nashville, TN, 37232, USA sug: subj: Pharmacokinetics Sampling Error Time Factors Human Simulations Models, Statistical Descriptive Statistics Metabolic Clearance Rate Data Analysis Software Funding Source ab: Purpose: Population pharmacokinetic (PK) data collected from routine clinical practice offers a rich source of valuable information. However, in observational population PK data, accurate time information for blood samples is often missing, resulting in measurement errors (ME) in the sampling time variable. The goal of this study was to investigate the effects on model parameters when a scheduled time is used instead of the actual blood sampling time, and to propose ME correction methods. Methods: Simulation studies were conducted based on two major factors: the curvature in PK profiles and the size of ME. As ME correction methods, transform both sides (TBS) models were developed with application of Box-Cox power transformation and Taylor expansion. The TBS models were compared to a conventional population PK model using simulations. Results: The most important determinant of bias due to time ME was the degree of curvature (nonlinearity) in PK profiles; the smaller the curvature around sampling times, the smaller the associated bias. The second important determinant was the magnitude of ME; the larger the ME, the larger the bias. The proposed TBS models performed better than a conventional population PK modeling when curvature and ME were substantial. Conclusions: Time ME in sampling time can lead to bias on the parameter estimators. The following practical recommendations are provided: 1) when the curvature of PK profiles is small, conventional population PK modeling is robust to even large ME; and 2) when the curvature is moderate or large, the proposed methodology reduces bias in parameter estimates. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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