NDLI: Sample Size Computations for PK/PD Population Models

Content Provider	Springer Nature Link
Author	Kang, Dongwoo Schwartz, Janice B. Verotta, Davide
Copyright Year	2005
Abstract	We describe an accurate, yet simple and fast sample size computation method for hypothesis testing in population PK/PD studies. We use a first order approximation to the nonlinear mixed effects model and chi-square distributed Wald statistic to compute the minimum sample size to achieve given degree of power in rejecting a null hypothesis in population PK/PD studies. The method is an extension of Rochon’s sample size computation method for repeated measurement experiments. We compute sample sizes for PK and PK/PD models with different conditions, and use Monte Carlo simulation to show that the computed sample size retrieves the required power. We also show the effect of different sampling strategies, such as minimal, i.e., as many observations per individual as parameters in the model, and intensive on sample size. The proposed sample size computation method can produce estimates of minimum sample size to achieve the desired power in hypothesis testing in a greatly reduced time than currently available simulation-based methods. The method is rapid and efficient for sample size computation in population PK/PD study using nonlinear mixed effect models. The method is general and can accommodate any type of hierarchical models. Simulation results suggest that intensive sampling allows the reduction of the number of patients enrolled in a clinical study.
Starting Page	685
Ending Page	701
Page Count	17
File Format	PDF
ISSN	1567567X
Journal	Journal of Pharmacokinetics and Pharmacodynamics
Volume Number	32
Issue Number	5-6
e-ISSN	15738744
Language	English
Publisher	Kluwer Academic Publishers-Plenum Publishers
Publisher Date	2005-01-01
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	sample size PK/PD models simulation methods Pharmacology/Toxicology Pharmacy Veterinary Medicine Biochemistry Biomedical Engineering
Content Type	Text
Resource Type	Article
Subject	Pharmacology

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