Document Detail


Evaluating pharmacokinetic/pharmacodynamic models using the posterior predictive check.
MedLine Citation:
PMID:  11381569     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
The posterior predictive check (PPC) is a model evaluation tool. It assigns a value (pPPC) to the probability that the value of a given statistic computed from data arising under an analysis model is as or more extreme than the value computed from the real data themselves. If this probability is too small, the analysis model is regarded as invalid for the given statistic. Properties of the PPC for pharmacokinetic (PK) and pharmacodynamic (PD) model evaluation are examined herein for a particularly simple simulation setting: extensive sampling of a single individual's data arising from simple PK/PD and error models. To test the performance characteristics of the PPC, repeatedly, "real" data are simulated and for a variety of statistics, the PPC is applied to an analysis model, which may (null hypothesis) or may not (alternative hypothesis) be identical to the simulation model. Five models are used here: (PK1) mono-exponential with proportional error, (PK2) biexponential with proportional error, (PK2 epsilon) biexponential with additive error, (PD1) Emax model with additive error under the logit transform, and (PD2) sigmoid Emax model with additive error under the logit transform. Six simulation/analysis settings are studied. The first three, (PK1/PK1), (PK2/PK2), and (PD1/PD1) evaluate whether the PPC has appropriate type-I error level, whereas the second three (PK2/PK1), (PK2 epsilon/PK2), and (PD2/PD1) evaluate whether the PPC has adequate power. For a set of 100 data sets simulated/analyzed under each model pair according to a stipulated extensive sampling design, the pPPC is computed for a number of statistics in three different ways (each way uses a different approximation to the posterior distribution on the model parameters). We find that in general; (i) The PPC is conservative under the null in the sense that for many statistics, prob(pPPC < or = alpha) < alpha for small alpha. With respect to such statistics, this means that useful models will rarely be regarded incorrectly as invalid. A high correlation of a statistic with the parameter estimates obtained from the same data used to compute the statistic (a measure of statistical "sufficiency") tends to identify the most conservative statistics. (ii) Power is not very great, at least for the alternative models we tested, and it is especially poor with "statistics" that are in part a function of parameters as well as data. Although there is a tendency for nonsufficient statistics (as we have measured this) to have greater power, this is by no means an infallible diagnostic. (iii) No clear advantage for one or another method of approximating the posterior distribution on model parameters is found.
Authors:
Y Yano; S L Beal; L B Sheiner
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Publication Detail:
Type:  Journal Article; Research Support, U.S. Gov't, P.H.S.    
Journal Detail:
Title:  Journal of pharmacokinetics and pharmacodynamics     Volume:  28     ISSN:  1567-567X     ISO Abbreviation:  J Pharmacokinet Pharmacodyn     Publication Date:  2001 Apr 
Date Detail:
Created Date:  2001-05-30     Completed Date:  2001-12-04     Revised Date:  2007-11-14    
Medline Journal Info:
Nlm Unique ID:  101096520     Medline TA:  J Pharmacokinet Pharmacodyn     Country:  United States    
Other Details:
Languages:  eng     Pagination:  171-92     Citation Subset:  IM    
Affiliation:
Department of Biopharmaceutical Sciences, School of Pharmacy, University of California, San Francisco, San Francisco, California, USA.
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MeSH Terms
Descriptor/Qualifier:
Area Under Curve
Models, Biological*
Pharmacokinetics*
Pharmacology / methods*
Grant Support
ID/Acronym/Agency:
GM57183/GM/NIGMS NIH HHS

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


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