Can I read Evaluations of Bayesian and maximum likelihood methods in PK models with below-quantification-limit data on EtoBox?

Evaluations of Bayesian and maximum likelihood methods in PK models with below-quantification-limit data by Shuying Yang; James Roger is a Mathematics article available to read on EtoBox.

What is Evaluations of Bayesian and maximum likelihood methods in PK models with below-quantification-limit data about?

Pharmacokinetic (PK) data often contain concentration measurements below the quantification limit (BQL). While specific values cannot be assigned to these observations, nevertheless these observed BQL data are informative and generally known to be lower than the lower limit of quantification (LLQ). Setting BQLs as missing data violates the usual missing at random (MAR) assumption applied to the statistical methods, and therefore leads to biased or less precise parameter estimation. By definition, these data lie within the interval [0, LLQ], and can be considered as censored observations. Statistical methods that handle censored data, such as maximum likelihood and Bayesian methods, are thus useful in the modelling of such data sets. The main aim of this work was to investigate the impact of the amount of BQL observations on the bias and precision of parameter estimates in population PK models (non-linear mixed effects models in general) under maximum likelihood method as implemented in SAS and NONMEM, and a Bayesian approach using Markov chain Monte Carlo (MCMC) as applied in WinBUGS. A second aim was to compare these different methods in dealing with BQL or censored data in a prac

Who reads Evaluations of Bayesian and maximum likelihood methods in PK models with below-quantification-limit data?

Author

Shuying Yang; James Roger

Publisher

John Wiley and Sons; Wiley (John Wiley & Sons); John Wiley & Sons Inc.; Wiley (ISSN 1539-1604)

Published

2009

Language

EN

Field

Mathematics (Physical Sciences)