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Importance of shrinkage in empirical bayes estimates for diagnostics: problems and solutions
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (Farmakometri)
2009 (English)In: AAPS Journal, ISSN 1550-7416, Vol. 11, no 3, 558-569 p.Article in journal (Refereed) Published
Abstract [en]

Empirical Bayes ("post hoc") estimates (EBEs) of etas provide modelers with diagnostics: the EBEs themselves, individual prediction (IPRED), and residual errors (individual weighted residual (IWRES)). When data are uninformative at the individual level, the EBE distribution will shrink towards zero (eta-shrinkage, quantified as 1-SD(eta (EBE))/omega), IPREDs towards the corresponding observations, and IWRES towards zero (epsilon-shrinkage, quantified as 1-SD(IWRES)). These diagnostics are widely used in pharmacokinetic (PK) pharmacodynamic (PD) modeling; we investigate here their usefulness in the presence of shrinkage. Datasets were simulated from a range of PK PD models, EBEs estimated in non-linear mixed effects modeling based on the true or a misspecified model, and desired diagnostics evaluated both qualitatively and quantitatively. Identified consequences of eta-shrinkage on EBE-based model diagnostics include non-normal and/or asymmetric distribution of EBEs with their mean values ("ETABAR") significantly different from zero, even for a correctly specified model; EBE-EBE correlations and covariate relationships may be masked, falsely induced, or the shape of the true relationship distorted. Consequences of epsilon-shrinkage included low power of IPRED and IWRES to diagnose structural and residual error model misspecification, respectively. EBE-based diagnostics should be interpreted with caution whenever substantial eta- or epsilon-shrinkage exists (usually greater than 20% to 30%). Reporting the magnitude of eta- and epsilon-shrinkage will facilitate the informed use and interpretation of EBE-based diagnostics.

Place, publisher, year, edition, pages
2009. Vol. 11, no 3, 558-569 p.
Keyword [en]
empirical Bayes estimate, model building, model evaluation, NONMEM, shrinkage
National Category
Pharmaceutical Sciences
URN: urn:nbn:se:uu:diva-97517DOI: 10.1208/s12248-009-9133-0ISI: 000270544500018OAI: oai:DiVA.org:uu-97517DiVA: diva2:172496
Available from: 2008-09-12 Created: 2008-09-12 Last updated: 2010-07-06Bibliographically approved
In thesis
1. Improved pharmacometric model building techniques
Open this publication in new window or tab >>Improved pharmacometric model building techniques
2008 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Pharmacometric modelling is an increasingly used method for analysing the outcome from clinical trials in drug development. The model building process is complex and involves testing, evaluating and diagnosing a range of plausible models aiming to make an adequate inference from the observed data and predictions for future studies and therapy.

The aim of this thesis was to advance the approaches used in pharmacometrics by introducing improved models and methods for application in essential parts of model building procedure: (i) structural model development, (ii) stochastic model development and (iii) model diagnostics.

As a contribution to the structural model development, a novel flexible structural model for drug absorption, a transit compartment model, was introduced and evaluated. This model is capable of describing various drug absorption profiles and yet simple enough to be estimable from data available from a typical trial. As a contribution to the stochastic model development, three novel methods for parameter distribution estimation were developed and evaluated; a default NONMEM nonparametric method, an extended grid method and a semiparametric method with estimated shape parameters. All these methods are useful in circumstances when standard assumptions of parameter distributions in the population do not hold. The new methods provide less biased parameter estimates, better description of variability and better simulation properties of the model. As a contribution to model diagnostics, the most commonly used diagnostics were evaluated for their usefulness. In particular, diagnostics based on individual parameter estimates were systematically investigated and circumstances which are likely to misguide modelers towards making erroneous decisions in model development, relating to choice of structural, covariate and stochastic model components were identified.

In conclusion, novel approaches, insights and models have been provided to the pharmacometrics community.

Implementation of these advances to make model building more efficient and robust has been facilitated by development of diagnostic tools and automated routines.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2008. 98 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 80Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 80
Model building, Absorption model, Transit compartment model, Nonparametric method, Extended grid method, Semiparametric, Distribution transformation, Shrinkage, Model diagnostics
urn:nbn:se:uu:diva-9272 (URN)978-91-554-7275-7 (ISBN)
Public defence
2008-10-03, Room B41, BMC, Uppsala, 09:15
Available from: 2008-09-12 Created: 2008-09-12Bibliographically approved

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