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Improved Utilization of ADAS-cog Assessment Data through Item Response Theory based Pharmacometric Modeling
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)
Pfizer Inc. (Primary Care Business Unit)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (​Pharmacometrics Research Group)
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2014 (English)In: Pharmaceutical research, ISSN 0724-8741, E-ISSN 1573-904X, Vol. 31, no 8, 2152-2165 p.Article in journal (Refereed) Published
Abstract [en]

Purpose

This work investigates improved utilization of ADAS-cog data (the primaryoutcome in Alzheimer's disease (AD) trials of mild and moderate AD) by combiningpharmacometric modeling and item response theory (IRT).

Methods

A baseline IRT model characterizing the ADAS-cog was built based on datafrom 2744 individuals. Pharmacometric methods were used to extend the baseline IRTmodel to describe longitudinal ADAS-cog scores from an 18-month clinical study with322 patients. Sensitivity of the ADAS-cog items in different patient populations as wellas the power to detect a drug effect in relation to total score base methods wereassessed with IRT based models.

Results

IRT analysis was able to describe both total and item level baseline ADAS-cogdata. Longitudinal data were also well described. Differences in the informationcontent of the item level components could be quantitatively characterized and rankedfor mild cognitively impairment and mild AD populations. Based on clinical trialsimulations with a theoretical drug effect, the IRT method demonstrated a significantlyhigher power to detect drug effect compared to the traditional method of analysis.

Conclusion

A combined framework of IRT and pharmacometric modeling permits amore effective and precise analysis than total score based methods and thereforeincreases the value of ADAS-cog data.

Place, publisher, year, edition, pages
Springer, 2014. Vol. 31, no 8, 2152-2165 p.
Keyword [en]
Alzheimer's disease, Item response theory, ADAS-cog, pharmacometrics, nonlinear mixed effect models
National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-216524DOI: 10.1007/s11095-014-1315-5ISI: 000341712400026OAI: oai:DiVA.org:uu-216524DiVA: diva2:690155
Funder
EU, FP7, Seventh Framework Programme, 115156
Available from: 2014-01-22 Created: 2014-01-22 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

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

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