Practical recommendations for population PK studies with sampling time errors
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PHARMACOKINETICS AND DISPOSITION
Practical recommendations for population PK studies with sampling time errors Leena Choi & Ciprian M. Crainiceanu & Brian S. Caffo
Received: 13 June 2013 / Accepted: 7 August 2013 / Published online: 24 August 2013 # Springer-Verlag Berlin Heidelberg 2013
Abstract 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. Electronic supplementary material The online version of this article (doi:10.1007/s00228-013-1576-7) contains supplementary material, which is available to authorized users. L. Choi (*) Department of Biostatistics, School of Medicine, Vanderbilt University, 1161 21st Avenue South, Medical Center North, S-2323, Nashville, TN 37232, USA e-mail: [email protected] C. M. Crainiceanu : B. S. Caffo Department of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, 615 North Wolfe Street, Baltimore, MD 21205, USA
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. Keywords Nonlinear mixed effect model . Pharmacokinetic . Measurement error . Berkson error . Blood sampling time . Blood sampling design
Introduction Observational population pharmacokinetic (PK) data collected during routine clinical practice is a potentially rich source of valuable information. In contrast to PK data collected from a clinical trial or a well-controlled environment, the analysis of observational population PK data poses several statistical challenges. Our focus lies on the issue of measurement error (ME) in blood sampling times in population PK modeling, which can occur for
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