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D Optimal Designs for Three Poisson Dose-Response Models
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (farmakometri)
Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Pharmacy, Department of Pharmaceutical Biosciences. (farmakometri)
Astellas Pharma Europe, Leiderdorp, The Netherlands.
2013 (English)In: Journal of Pharmacokinetics and Pharmacodynamics, ISSN 1567-567X, E-ISSN 1573-8744, Vol. 40, no 2, 201-211 p.Article in journal (Refereed) Published
Abstract [en]

The objective of this paper was to find and investigate the performance of the D optimal designs for three Poisson dose-response models. Phase II dose ranging studies are pivotal in the drug development program, being used to select dose(s) for phase III. Count data is encountered in a number of clinical areas. The Poisson distribution provides an intuitive platform for modelling such data, especially when combined with random effects which allow subjects to differ in their response rates. This work investigated three Poisson dose-response models of increasing complexity. A simple Emax model was used to describe the drug effect, and D optimal designs under a range of different parameter values (scenarios) were found. The relative performances between scenarios were assessed using: the precision of all parameters, the precision of the drug effect parameters, and the percent coefficient of variation (%CV) of the ED50 parameter. The results showed that the D optimal designs were similar across models and scenarios, with the D optimal designs consisting of placebo, the maximum dose, and a dose just below the ED50. However the relative performance of the optimal designs was very different. For example, with 1000 subjects, the %CV of the ED50 parameter ranged from 1.4% to 91%. Performance typically improved with higher baseline counts, smaller random effects, and larger Emax. This work introduces a framework for determining and evaluating the performance of D optimal designs for phase II dose ranging studies with count data as the primary endpoint.

Place, publisher, year, edition, pages
Springer, 2013. Vol. 40, no 2, 201-211 p.
Keyword [en]
Poisson, Count, Optimal, Emax model, Phase II, Dose-response
National Category
Pharmaceutical Sciences
URN: urn:nbn:se:uu:diva-182276DOI: 10.1007/s10928-013-9300-xISI: 000317974200006OAI: oai:DiVA.org:uu-182276DiVA: diva2:559317
Available from: 2012-10-08 Created: 2012-10-08 Last updated: 2016-01-15
In thesis
1. Optimal (Adaptive) Design and Estimation Performance in Pharmacometric Modelling
Open this publication in new window or tab >>Optimal (Adaptive) Design and Estimation Performance in Pharmacometric Modelling
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

The pharmaceutical industry now recognises the importance of the newly defined discipline of pharmacometrics. Pharmacometrics uses mathematical models to describe and then predict the performance of new drugs in clinical development. To ensure these models are useful, the clinical studies need to be designed such that the data generated allows the model predictions to be sufficiently accurate and precise. The capability of the available software to reliably estimate the model parameters must also be well understood. 

This thesis investigated two important areas in pharmacometrics: optimal design and software estimation performance. The three optimal design papers progressed significant areas of optimal design research, especially relevant to phase II dose response designs. The use of exposure, rather than dose, was investigated within an optimal design framework. In addition to using both optimal design and clinical trial simulation, this work employed a wide range of metrics for assessing design performance, and was illustrative of how optimal designs for exposure response models may yield dose selections quite different to those based on standard dose response models. The investigation of the optimal designs for Poisson dose response models demonstrated a novel mathematical approach to the necessary matrix calculations for non-linear mixed effects models. Finally, the enormous potential of using optimal adaptive designs over fixed optimal designs was demonstrated. The results showed how the adaptive designs were robust to initial parameter misspecification, with the capability to "learn" the true dose response using the accruing subject data. The two estimation performance papers investigated the relative performance of a number of different algorithms and software programs for two complex pharmacometric models.

In conclusion these papers, in combination, cover a wide spectrum of study designs for non-linear dose/exposure response models, covering: normal/non-normal data, fixed/mixed effect models, single/multiple design criteria metrics, optimal design/clinical trial simulation, and adaptive/fixed designs. 

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2012. 76 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Pharmacy, ISSN 1651-6192 ; 166
Phase II, dose response, optimal design, adaptive design, exposure response, count data
National Category
Pharmaceutical Sciences
urn:nbn:se:uu:diva-182284 (URN)978-91-554-8491-0 (ISBN)
Public defence
2012-11-30, B41, Biomedicinskt Centrum, Husargatan 3, Uppsala, 09:15 (English)
Available from: 2012-11-02 Created: 2012-10-08 Last updated: 2013-01-23Bibliographically approved

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