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Accelerating Monte-Carlo Power Studies through Parametric Power Estimation
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)
(English)Manuscript (preprint) (Other academic)
Abstract [en]

Estimating the power of a future clinical study is a common problem in the drug development process. Within the framework of model based drug development this problem is solved through Monte-Carlo studies where numerous replicates of the trial are simulated and subsequently analysed. This process can be very time consuming due to the high number of replicates required to obtain a stable power estimate. Non-linear mixed effect models which are frequently used for the analysis of clinical trial data are especially problematic as they can have a run time of several hours.

A novel parametric power estimation (PPE) algorithm utilizing the theoretical distribution of the alternative hypothesis is presented in this work and compared to classical Monte-Carlo studies. The PPE algorithm estimates the unknown non-centrality parameter in the theoretical distribution from a limited number of Monte-Carlo simulation and estimations. Furthermore, from the estimated parameter a complete power versus sample size curve can be obtained analytically without additional simulations. The PPE and classical Monte-Carlo algorithms were compared for 3 different drug development examples.

For a single power calculation, given a specific sample size, the PPE algorithm provided accurate estimates for all investigated scenarios and required 2 times fewer samples than the pure Monte-Carlo method to achieve the same level of precision. Furthermore, from this single power calculation, the PPE method can derive an entire power curve (power versus sample size), drastically reducing run times for this computation. The power curves from the PPE algorithm were in excellent agreement with the curves obtained using classical Monte-Carlo techniques.

National Category
Pharmaceutical Sciences
Research subject
Pharmaceutical Science
Identifiers
URN: urn:nbn:se:uu:diva-216528OAI: oai:DiVA.org:uu-216528DiVA: diva2:690171
Available from: 2014-01-22 Created: 2014-01-22 Last updated: 2014-04-29
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, SebastianHooker, Andrew C.

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