title: Further Evaluation of Covariate Analysis using Empirical Bayes Estimates in Population Pharmacokinetics: the Perception of Shrinkage and Likelihood Ratio Test.
abstract: Covariate analysis based on population pharmacokinetics (PPK) is used to identify clinically relevant factors. The likelihood ratio test (LRT) based on nonlinear mixed effect model fits is currently recommended for covariate identification, whereas individual empirical Bayesian estimates (EBEs) are considered unreliable due to the presence of shrinkage. The objectives of this research were to investigate the type I error for LRT and EBE approaches, to confirm the similarity of power between the LRT and EBE approaches from a previous report and to explore the influence of shrinkage on LRT and EBE inferences. Using an oral one-compartment PK model with a single covariate impacting on clearance, we conducted a wide range of simulations according to a two-way factorial design. The results revealed that the EBE-based regression not only provided almost identical power for detecting a covariate effect, but also controlled the false positive rate better than the LRT approach. Shrinkage of EBEs is likely not the root cause for decrease in power or inflated false positive rate although the size of the covariate effect tends to be underestimated at high shrinkage. In summary, contrary to the current recommendations, EBEs may be a better choice for statistical tests in PPK covariate analysis compared to LRT. We proposed a three-step covariate modeling approach for population PK analysis to utilize the advantages of EBEs while overcoming their shortcomings, which allows not only markedly reducing the run time for population PK analysis, but also providing more accurate covariate tests.
authors.affiliation: Janssen Research and Development, 920 Route 202, Raritan, New Jersey, 08869, USA. sxu26@its.jnj.com.
authors.forename: Xu Steven
authors.initials: XS
authors.lastname: Xu
authors.affiliation: School of public health administration, Anhui Medical University, Hefei, China.
authors.forename: Min
authors.initials: M
authors.lastname: Yuan
authors.affiliation: University of Florida, Gainesville, Florida, USA.
authors.forename: Haitao
authors.initials: H
authors.lastname: Yang
authors.affiliation: Bristol-Myers Squibb, Princeton, New Jersey, USA.
authors.forename: Yan
authors.initials: Y
authors.lastname: Feng
authors.affiliation: Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong, China.
authors.forename: Jinfeng
authors.initials: J
authors.lastname: Xu
authors.affiliation: Janssen Research and Development, 920 Route 202, Raritan, New Jersey, 08869, USA.
authors.forename: Jose
authors.initials: J
authors.lastname: Pinheiro
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003198:Computer Simulation / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D008954:Models, Biological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010599:Pharmacokinetics
keywords: covariate; empirical bayes estimates; likelihood ratio test; population pharmacokinetics; shrinkage