Document Detail

Prediction discrepancies for the evaluation of nonlinear mixed-effects models.
MedLine Citation:
PMID:  16284919     Owner:  NLM     Status:  MEDLINE    
Reliable estimation methods for non-linear mixed-effects models are now available and, although these models are increasingly used, only a limited number of statistical developments for their evaluation have been reported. We develop a criterion and a test to evaluate nonlinear mixed-effects models based on the whole predictive distribution. For each observation, we define the prediction discrepancy (pd) as the percentile of the observation in the whole marginal predictive distribution under H(0). We propose to compute prediction discrepancies using Monte Carlo integration which does not require model approximation. If the model is valid, these pd should be uniformly distributed over (0, 1) which can be tested by a Kolmogorov-Smirnov test. In a simulation study based on a standard population pharmacokinetic model, we compare and show the interest of this criterion with respect to the one most frequently used to evaluate nonlinear mixed-effects models: standardized prediction errors (spe) which are evaluated using a first order approximation of the model. Trends in pd can also be evaluated via several plots to check for specific departures from the model.
France Mentré; Sylvie Escolano
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Publication Detail:
Type:  Evaluation Studies; Journal Article     Date:  2005-11-13
Journal Detail:
Title:  Journal of pharmacokinetics and pharmacodynamics     Volume:  33     ISSN:  1567-567X     ISO Abbreviation:  J Pharmacokinet Pharmacodyn     Publication Date:  2006 Jun 
Date Detail:
Created Date:  2006-07-07     Completed Date:  2006-09-26     Revised Date:  2009-11-18    
Medline Journal Info:
Nlm Unique ID:  101096520     Medline TA:  J Pharmacokinet Pharmacodyn     Country:  England    
Other Details:
Languages:  eng     Pagination:  345-67     Citation Subset:  IM    
INSERM, U738, 46 rue Henri Huchard, Paris, France.
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MeSH Terms
Computer Simulation
Likelihood Functions
Models, Biological*
Models, Statistical*
Monte Carlo Method
Statistical Distributions

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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