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Non-Bayesian Knowledge Propagation using Model Model-Based Analysis of Data from Multiple Clinical Studies
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2008 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 35, no 1, 117-137 p.Article in journal (Refereed) Published
Abstract [en]

The ultimate goal in drug development is to establish the manner of safe and efficacious administration to patients. To achieve this in an efficient way the information contained in the clinical studies should contribute to the increasing pool of accumulated knowledge. The aim of this simulation study is to investigate different knowledge-propagation strategies when the data is analysed using a model-based approach in NONMEM. Pharmacokinetic studies were simulated according to several scenarios of the underlying model and study design, including a population-optimal design based on analysis of a previous study. Five approaches with different degrees of knowledge propagation were investigated: analysing the studies pooled into one dataset, merging the results from analysing the studies separately, fitting a pre-specified model that has been selected from a previous study on either the most recent study or on the pooled dataset, or naively analysing the most recent study without any regards to any previous study. The approaches were evaluated on what model was selected (qualitative knowledge, investigated by stepwise covariate selection within NONMEM) as well as parameter precision (quantitative knowledge) and predictive performance of the model. Pooling all studies into one dataset is the best approach for identifying the correct model and obtaining good predictive performance and merging the results of separate analyses may perform almost as well. Fitting a pre-specified model on new data is fast, without selection bias, and sanctioned for model-based confirmatory analyses. However, fitting the same pre-specified model to all available data is still fast and can be expected to perform better in terms of predictive performance than the unbiased alternative. Using ED-optimal design of sample times and stratification of subjects from different subgroups is a successful strategy which allows sparse sampling and handles prior parameter uncertainty.

Place, publisher, year, edition, pages
2008. Vol. 35, no 1, 117-137 p.
Keyword [en]
population pharmacokinetics, meta-analysis, learning, confirming, population-optimal design, data pooling, predictive modelling, model selection, population modelling
National Category
Pharmaceutical Sciences
URN: urn:nbn:se:uu:diva-95984DOI: 10.1007/s10928-007-9079-8ISI: 000253994800006PubMedID: 17990085OAI: oai:DiVA.org:uu-95984DiVA: diva2:170385
Available from: 2007-05-15 Created: 2007-05-15 Last updated: 2011-03-03Bibliographically approved
In thesis
1. Covariate Model Building in Nonlinear Mixed Effects Models
Open this publication in new window or tab >>Covariate Model Building in Nonlinear Mixed Effects Models
2007 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Population pharmacokinetic-pharmacodynamic (PK-PD) models can be fitted using nonlinear mixed effects modelling (NONMEM). This is an efficient way of learning about drugs and diseases from data collected in clinical trials. Identifying covariates which explain differences between patients is important to discover patient subpopulations at risk of sub-therapeutic or toxic effects and for treatment individualization. Stepwise covariate modelling (SCM) is commonly used to this end. The aim of the current thesis work was to evaluate SCM and to develop alternative approaches. A further aim was to develop a mechanistic PK-PD model describing fasting plasma glucose, fasting insulin, insulin sensitivity and beta-cell mass.

The lasso is a penalized estimation method performing covariate selection simultaneously to shrinkage estimation. The lasso was implemented within NONMEM as an alternative to SCM and is discussed in comparison with that method. Further, various ways of incorporating information and propagating knowledge from previous studies into an analysis were investigated. In order to compare the different approaches, investigations were made under varying, replicated conditions. In the course of the investigations, more than one million NONMEM analyses were performed on simulated data. Due to selection bias the use of SCM performed poorly when analysing small datasets or rare subgroups. In these situations, the lasso method in NONMEM performed better, was faster, and additionally validated the covariate model. Alternatively, the performance of SCM can be improved by propagating knowledge or incorporating information from previously analysed studies and by population optimal design.

A model was also developed on a physiological/mechanistic basis to fit data from three phase II/III studies on the investigational drug, tesaglitazar. This model described fasting glucose and insulin levels well, despite heterogeneous patient groups ranging from non-diabetic insulin resistant subjects to patients with advanced diabetes. The model predictions of beta-cell mass and insulin sensitivity were well in agreement with values in the literature.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2007. 77 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 59
Pharmacokinetics/Pharmacotherapy, Pharmacokinetics, Pharmacodynamics, Modeling, Covariate selection, Stepwise selection, Covariate analysis, Methodology, Model validation, Model evaluation, Type-2 diabetes, Beta-cell function, Meta analysis, Cross-validation, Least absolute shrinkage and selection operator, Pharmacometrics, ED optimization, Farmakokinetik/Farmakoterapi
urn:nbn:se:uu:diva-7923 (URN)978-91-554-6915-3 (ISBN)
Public defence
2007-06-05, B41, BMC, Husarg. 3, Uppsala, 09:15
Available from: 2007-05-15 Created: 2007-05-15 Last updated: 2010-12-09Bibliographically approved

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