Optimizing disease progression study designs for drug effect discrimination

Ueckert, Sebastian, Hennig, Stefanie, Nyberg, Joakim, Karlsson, Mats O. and Hooker, Andrew C. (2013) Optimizing disease progression study designs for drug effect discrimination. Journal of Pharmacokinetics and Pharmacodynamics, 40 5: 587-596. doi:10.1007/s10928-013-9331-3

Author Ueckert, Sebastian
Hennig, Stefanie
Nyberg, Joakim
Karlsson, Mats O.
Hooker, Andrew C.
Title Optimizing disease progression study designs for drug effect discrimination
Journal name Journal of Pharmacokinetics and Pharmacodynamics   Check publisher's open access policy
ISSN 1567-567X
Publication date 2013-10-01
Year available 2013
Sub-type Article (original research)
DOI 10.1007/s10928-013-9331-3
Volume 40
Issue 5
Start page 587
End page 596
Total pages 10
Place of publication New York, NY, United States
Publisher Springer
Language eng
Abstract Investigate the possibility to directly optimize a clinical trial design for statistical power to detect a drug effect and compare to optimal designs that focus on parameter precision. An improved statistic derived from the general formulation of the Wald approximation was used to predict the statistical power for given trial designs of a disease progression study. The predicted value was compared, together with the classical Wald statistic, to a type I error-corrected model-based power determined via clinical trial simulations. In a second step, a study design for maximal power was determined by directly maximizing the new statistic. The resulting power-optimal designs and their corresponding performance based on empirical power calculations were compared to designs focusing on parameter precision. Comparisons of empirically determined power and the newly developed statistic, showed excellent agreement across all scenarios investigated. This was in contrast to the classical Wald statistic, which consistently over-predicted the reference power with deviations of up to 90 %. Designs maximized using the proposed metric differed from traditional optimal designs and showed equal or up to 20 % higher power in the subsequent clinical trial simulations. Furthermore, the proposed method was used to minimize the number of individuals required to achieve 80 % power through a simultaneous optimization of study size and study design. The targeted power of 80 % was confirmed in subsequent simulation study. A new statistic was developed, allowing for the explicit optimization of a clinical trial design with respect to statistical power.
Keyword Optimal experimental design
Statistical power
Wald test
Disease progression studies
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Published online: 27 August 2013.

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Pharmacy Publications
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Created: Mon, 22 Jul 2013, 21:03:40 EST by Dr Stefanie Hennig on behalf of School of Pharmacy