Current applications of cross validation have been unsuccessful at identifying covariate effects in the population Pharmacokinetic/Pharmacodynamic (PK/PD) setting when other methods find a covariate effect may exist. Software that does population PK/PD modeling has a nice feature of being able to do a post hoc step without any major iterations to obtain Bayesian parameter estimates and hence predictions for subjects that were not in the dataset that was used to fit the model. This work proposes cross validation methods for longitudinal mixed effects models that are effective at identifying covariate effects when they exist.
|Commitee:||Bair, Eric, Ivanova, Anastasia, Koch, Gary, Kosorok, Michael, Weiner, Daniel|
|School:||The University of North Carolina at Chapel Hill|
|School Location:||United States -- North Carolina|
|Source:||DAI-B 74/05(E), Dissertation Abstracts International|
|Subjects:||Biostatistics, Applied Mathematics, Pharmacology|
|Keywords:||Cross validation, Nonlinear mixed effects, Pharmacodynamics, Pharmacokinetics, Population|
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