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Evaluation of Bias, Precision, Robustness and Runtime for Estimation Methods in NONMEM 7
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (​Pharmacometrics Research Group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (​Pharmacometrics Research Group)ORCID iD: 0000-0002-3712-0255
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (​Pharmacometrics Research Group)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (​Pharmacometrics Research Group)ORCID iD: 0000-0002-2676-5912
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2014 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 41, no 3, 223-238 p.Article in journal (Refereed) Published
Abstract [en]

NONMEM is the most widely used software for population pharmacokinetic (PK)-pharmacodynamic (PD) analyses. The latest version, NONMEM 7 (NM7), includes several sampling-based estimation algorithms in addition to the classical algorithms. In this study, performance of the estimation algorithms available in NM7 was investigated with respect to bias, precision, robustness and runtime for a diverse set of PD models. Simulations of 500 data sets from each PD model were reanalyzed with the available estimation algorithms to investigate bias and precision. Simulations of 100 data sets were used to investigate robustness by comparing final estimates obtained after estimations starting from the true parameter values and initial estimates randomly generated using the CHAIN feature in NM7. Average estimation time for each algorithm and each model was calculated from the runtimes reported by NM7.

The algorithm giving the lowest bias and highest precision across models was importance sampling (IMP), closely followed by FOCE/LAPLACE and stochastic approximation expectation-maximization (SAEM). The algorithms relative robustness differed between models, but FOCE/LAPLACE was the most robust algorithm across models, followed by SAEM and IMP. FOCE/LAPLACE was also the algorithm with the shortest runtime for all models, followed by iterative two-stage (ITS). The Bayesian Markov Chain Monte Carlo method, used in this study for point estimation, performed worst in all tested metrics.

Place, publisher, year, edition, pages
2014. Vol. 41, no 3, 223-238 p.
Keyword [en]
NONMEM, estimation algorithms
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-216136DOI: 10.1007/s10928-014-9359-zISI: 000338496300003PubMedID: 24801864OAI: oai:DiVA.org:uu-216136DiVA: diva2:688965
Available from: 2014-01-19 Created: 2014-01-19 Last updated: 2017-12-06Bibliographically approved
In thesis
1. Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies
Open this publication in new window or tab >>Novel Pharmacometric Methods for Design and Analysis of Disease Progression Studies
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

With societies aging all around the world, the global burden of degenerative diseases is expected to increase exponentially. From the perspective drug development, degenerative diseases represent an especially challenging class. Clinical trials, in this context often termed disease progression studies, are long, costly, require many individuals, and have low success rates. Therefore, it is crucial to use informative study designs and to analyze efficiently the obtained trial data. The development of novel approaches intended towards facilitating both the design and the analysis of disease progression studies was the aim of this thesis.

This aim was pursued in three stages (i) the characterization and extension of pharmacometric software, (ii) the development of new methodology around statistical power, and (iii) the demonstration of application benefits.

The optimal design software PopED was extended to simplify the application of optimal design methodology when planning a disease progression study. The performance of non-linear mixed effect estimation algorithms for trial data analysis was evaluated in terms of bias, precision, robustness with respect to initial estimates, and runtime. A novel statistic allowing for explicit optimization of study design for statistical power was derived and found to perform superior to existing methods. Monte-Carlo power studies were accelerated through application of parametric power estimation, delivering full power versus sample size curves from a few hundred Monte-Carlo samples. Optimal design and an explicit optimization for statistical power were applied to the planning of a study in Alzheimer's disease, resulting in a 30% smaller study size when targeting 80% power. The analysis of ADAS-cog score data was improved through application of item response theory, yielding a more exact description of the assessment score, an increased statistical power and an enhanced insight in the assessment properties.

In conclusion, this thesis presents novel pharmacometric methods that can help addressing the challenges of designing and planning disease progression studies.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 65 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 184
Keyword
pharmacometrics, optimal design, non-linear mixed effects models, degenerative diseases, Alzheimer's disease, item response theory, statistical power
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-216537 (URN)978-91-554-8862-8 (ISBN)
Public defence
2014-03-07, B41, Biomedicinskt Centrum, Husargatan 3, Uppsala, 13:15 (English)
Opponent
Supervisors
Available from: 2014-02-13 Created: 2014-01-22 Last updated: 2014-04-29
2. Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling
Open this publication in new window or tab >>Methodology for Handling Missing Data in Nonlinear Mixed Effects Modelling
2014 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

To obtain a better understanding of the pharmacokinetic and/or pharmacodynamic characteristics of an investigated treatment, clinical data is often analysed with nonlinear mixed effects modelling. The developed models can be used to design future clinical trials or to guide individualised drug treatment. Missing data is a frequently encountered problem in analyses of clinical data, and to not venture the predictability of the developed model, it is of great importance that the method chosen to handle the missing data is adequate for its purpose. The overall aim of this thesis was to develop methods for handling missing data in the context of nonlinear mixed effects models and to compare strategies for handling missing data in order to provide guidance for efficient handling and consequences of inappropriate handling of missing data.

In accordance with missing data theory, all missing data can be divided into three categories; missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). When data are MCAR, the underlying missing data mechanism does not depend on any observed or unobserved data; when data are MAR, the underlying missing data mechanism depends on observed data but not on unobserved data; when data are MNAR, the underlying missing data mechanism depends on the unobserved data itself.

Strategies and methods for handling missing observation data and missing covariate data were evaluated. These evaluations showed that the most frequently used estimation algorithm in nonlinear mixed effects modelling (first-order conditional estimation), resulted in biased parameter estimates independent on missing data mechanism. However, expectation maximization (EM) algorithms (e.g. importance sampling) resulted in unbiased and precise parameter estimates as long as data were MCAR or MAR. When the observation data are MNAR, a proper method for handling the missing data has to be applied to obtain unbiased and precise parameter estimates, independent on estimation algorithm.

The evaluation of different methods for handling missing covariate data showed that a correctly implemented multiple imputations method and full maximum likelihood modelling methods resulted in unbiased and precise parameter estimates when covariate data were MCAR or MAR. When the covariate data were MNAR, the only method resulting in unbiased and precise parameter estimates was a full maximum likelihood modelling method where an extra parameter was estimated, correcting for the unknown missing data mechanism's dependence on the missing data.

This thesis presents new insight to the dynamics of missing data in nonlinear mixed effects modelling. Strategies for handling different types of missing data have been developed and compared in order to provide guidance for efficient handling and consequences of inappropriate handling of missing data.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2014. 75 p.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 189
Keyword
Pharmacometrics, population models, censored observations, missing covariates, missing dependent variable, missing data mechanism, missing completely at random (MCAR), missing at random (MAR), missing not at random (MNAR), estimation algorithms
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
urn:nbn:se:uu:diva-224098 (URN)978-91-554-8970-0 (ISBN)
Public defence
2014-08-29, B41, BMC, Husargatan 3, Uppsala, 09:15 (English)
Opponent
Supervisors
Available from: 2014-05-27 Created: 2014-05-03 Last updated: 2014-06-30Bibliographically approved

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Johansson, Åsa M.Ueckert, SebastianPlan, Elodie L.Hooker, Andrew C.Karlsson, Mats O.

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