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The Lasso – A Novel Method for Predictive Covariate Model Building in Nonlinear Mixed Effects Models
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.
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences.
2007 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 34, no 4, 485-517 p.Article in journal (Refereed) Published
Abstract [en]

Covariate models for population pharmacokinetics and pharmacodynamics are often built with a stepwise covariate modelling procedure (SCM). When analysing a small dataset this method may produce a covariate model that suffers from selection bias and poor predictive performance. The lasso is a method suggested to remedy these problems. It may also be faster than SCM and provide a validation of the covariate model. The aim of this study was to implement the lasso for covariate selection within NONMEM and to compare this method to SCM. In the lasso all covariates must be standardised to have zero mean and standard deviation one. Subsequently, the model containing all potential covariate–parameter relations is fitted with a restriction: the sum of the absolute covariate coefficients must be smaller than a value, t. The restriction will force some coefficients towards zero while the others are estimated with shrinkage. This means in practice that when fitting the model the covariate relations are tested for inclusion at the same time as the included relations are estimated. For a given SCM analysis, the model size depends on the P-value required for selection. In the lasso the model size instead depends on the value of t which can be estimated using cross-validation. The lasso was implemented as an automated tool using PsN. The method was compared to SCM in 16 scenarios with different dataset sizes, number of investigated covariates and starting models for the covariate analysis. Hundred replicate datasets were created by resampling from a PK-dataset consisting of 721 stroke patients. The two methods were compared primarily on the ability to predict external data, estimate their own predictive performance (external validation), and on the computer run-time. In all 16 scenarios the lasso predicted external data better than SCM with any of the studied P-values (5%, 1% and 0.1%), but the benefit was negligible for large datasets. The lasso cross-validation provided a precise and nearly unbiased estimate of the actual prediction error. On a single processor, the lasso was faster than SCM. Further, the lasso could run completely in parallel whereas SCM must run in steps. In conclusion, the lasso is superior to SCM in obtaining a predictive covariate model on a small dataset or on small subgroups (e.g. rare genotype). Run in parallel the lasso could be much faster than SCM. Using cross-validation, the lasso provides a validation of the covariate model and does not require the user to specify a P-value for selection.

Place, publisher, year, edition, pages
2007. Vol. 34, no 4, 485-517 p.
Keyword [en]
Least absolute shrinkage and selection operator, Covariate analysis, Model validation, Model evaluation, Data splitting, Predictive modelling, Mixed effects modelling
National Category
Pharmaceutical Sciences
Identifiers
URN: urn:nbn:se:uu:diva-95983DOI: 10.1007/s10928-007-9057-1ISI: 000248006800003PubMedID: 17516152OAI: oai:DiVA.org:uu-95983DiVA: diva2:170384
Available from: 2007-05-15 Created: 2007-05-15 Last updated: 2011-01-28Bibliographically 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.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 59
Keyword
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
Identifiers
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
Opponent
Supervisors
Available from: 2007-05-15 Created: 2007-05-15 Last updated: 2010-12-09Bibliographically approved

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