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Abstract

This paper describes a conceptual empirical Bayes framework for random effects modeling and estimation of population kinetic/dynamic parameters. Using this framework, one can easily see that the random effects formulation does not require unique parameter estimates for each individual based strictly on data for that individual. This approach is particularly useful in situations involving sparse data. A general solution to the estimation problem via the EM algorithm is presented and compared with other methods including those used in the NONMEM (Nonlinear Mixed Effects Modeling) package. Key areas that need further investigation, such as model validation and identification of design limitations, are pointed out.

Keywords

Population PK/PD analysis Random effects models Empirical Bayes procedure EM algorithm NONMEM

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Random Effects Modeling in Population: Kinetic/Dynamic Analysis

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