Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects

Performance comparison of first-order conditional estimation with interaction and Bayesian estimation methods for estimating the population parameters and its distribution from data sets with a low number of subjects

Abstract Background Exploratory preclinical, as well as clinical trials, may involve a small number of patients, making it difficult to calculate and analyze the pharmacokinetic (PK) parameters, especially if the PK parameters show very high inter-individual variability (IIV). In this study, the per...

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Bibliographic Details
Main Authors: Sudeep Pradhan, Byungjeong Song, Jaeyeon Lee, Jung-woo Chae, Kyung Im Kim, Hyun-moon Back, Nayoung Han, Kwang-il Kwon, Hwi-yeol Yun
Format: Article
Language:English
Published: BMC 2017-12-01
Series:BMC Medical Research Methodology
Subjects:
Estimation methods
Few subjects
First-order conditional estimation with interaction
Markov chain Monte Carlo Bayesian
NONMEM
Online Access:http://link.springer.com/article/10.1186/s12874-017-0427-0

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http://link.springer.com/article/10.1186/s12874-017-0427-0

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