title: Optimizing disease progression study designs for drug effect discrimination.
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.
authors: Pharmacometrics Research Group, Department of Pharmaceutical Biosciences, Faculty of Pharmacy, Uppsala University, P.O. Box 591, 751 24, Uppsala, Sweden, sebastian.ueckert@farmbio.uu.se.
authors: Sebastian
authors: S
authors: Ueckert
authors: Stefanie
authors: S
authors: Hennig
authors: Joakim
authors: J
authors: Nyberg
authors: Mats O
authors: MO
authors: Karlsson
authors: Andrew C
authors: AC
authors: Hooker
mesh_terms: D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D018450:Disease Progression; D004358:Drug Therapy / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D011237:Predictive Value of Tests; D012107:Research Design
references: Clin Pharmacol Ther. 2009 Oct;86(4):365-7
references: 19763117
references: Comput Methods Programs Biomed. 2005 Sep;79(3):241-57
references: 16023764
references: Osteoporos Int. 1997;7(5):407-13
references: 9425497
references: J Biopharm Stat. 2003 May;13(2):209-27
references: 12729390
references: Science. 2003 Nov 14;302(5648):1172-5
references: 14615528
references: J Clin Pharmacol. 2009 Mar;49(3):323-35
references: 19246730
references: Clin Pharmacol Ther. 2002 Dec;72(6):603-14
references: 12496742
references: J Pharmacokinet Pharmacodyn. 2010 Feb;37(1):67-83
references: 19967433
references: Annu Rev Pharmacol Toxicol. 2001;41:625-59
references: 11264471
references: Comput Methods Programs Biomed. 2012 Nov;108(2):789-805
references: 22640817
references: Curr Opin Neurol. 2008 Aug;21(4):454-60
references: 18607207
references: Pharm Res. 2005 Jul;22(7):1038-49
references: 16028004
references: Nat Rev Drug Discov. 2004 Aug;3(8):711-5
references: 15286737
references: Stat Med. 2007 Dec 10;26(28):5162-79
references: 17486667
references: Comput Methods Programs Biomed. 2004 Apr;74(1):29-46
references: 14992824
references: J Pharmacokinet Pharmacodyn. 2009 Apr;36(2):125-45
references: 19319484