Abstract
Background and objective
Cost is an extremely important factor to consider when planning drug clinical trials. Higher-order crossover designs have recently drawn considerable attention in comparative bioavailability studies because of their desirable statistical properties. In this paper, we compared the cost efficiency of five commonly used higher-order crossover designs under certain cost function for comparative bioavailability studies.
Methods
Multivariate normal data were simulated under scenarios of a wide range of variability and correlations (coefficient of variation = 10–40%; correlation coefficient ρ = 0.2–0.8). Monte Carlo simulations and mixed-effects models were carried out to obtain empirical sample sizes for each design using Schuirmann’s two-one sided test procedure, under an 80% power and a 5% significance level, based on the US FDA bioequivalence criteria (80–125%). The five crossover designs studied were the two-period four-sequence (D2 × 4), the three-period two-sequence (D3 × 2), the three-period four-sequence (D3 × 4), the four-period two-sequence (D4 × 2), and the four-period four-sequence (D4 × 4). Costs for each design were then determined by a cost function, which takes into account costs for recruiting and screening, costs associated with period, and the overheads incurred for multiple sequences. Comparison of the costs for the above-mentioned designs was made under different scenarios.
Results
There was no single design uniformly dominating the others in terms of cost efficiency for comparative bioavailability studies. The designs D3 × 2 and D4 × 4 (especially the former) have the best overall performance in terms of cost efficiency for comparative bioavailability studies. They dominated the other designs under most of the scenarios. The design D2 × 4 showed the worst performance among the five crossover designs.
Conclusions
A D3 × 2, and D4 × 2 crossover designs are recommended to achieve cost efficiency with a given power. The D2 × 4 crossover design is not recommended in general for comparative bioavailability studies.
Similar content being viewed by others
References
Huang S-M, Lesko LJ, Williams RL. Assessment of the quality and quantity of drug-drag interaction studies in recent NDA submissions: study design and data analysis issues. J Clin Pharmacol 1999; 39: 1006–14
Jones B, Kenward MG. Design and analysis of cross-over trials. New York (NY): Chapman & Hall/CRC, 2003
Patterson SD, Jones B. Bioequivalence and the pharmaceutical industry. Pharmaceutical Statistics 2002; 1: 83–95
Chow SC, Liu JP. Design and analysis of bioavailability and bioequivalence studies. New York (NY): Markel Dekker Inc, 2000
Cheng C, Wu C. Balanced repeated measurements designs. Annu Stat 1980; 8: 1272–83
Laska E, Meisner M, Kushner HB. Optimal crossover designs in the presence of carryover effects. Biometrics 1983; 39: 1087–91
Laska EM, Meisner M. A variational approach to optimal two-treatment crossover designs: applications to carry-over effect models. J Am Stat Assoc 1985; 80: 704–10
Brown BW. The crossover experiment for clinical trials. Biometrics 1980; 36: 69–79
Carriere KC, Huang R. Crossover designs for two-treatment clinical trials. J Stat Plan Inference 2000; 87: 125–34
Zhou J, Coate B, He Y. Empirical power computation using SAS for Schuirmann’s two one-sided tests procedure in clinical pharmacokinetic drug-drug interaction studies [poster]. SUGI 29; 2004 May 9–12; Montreal (QC)
Zhou J, Yuan Y, Chen M, et al. Empirical power for higher-order crossover designs in comparative bioavailability clinical trials [poster]. SUGI 30; 2005 Apr 10–13, Philadelphia (PA)
Acknowledgements
The research work was supported by and carried out at Pfizer Inc., where the first author was an Associate Director at the Department of Clinical Biostatistics.
We are grateful to Drs Randy Allred, Eric Yan and Alex Kuperman at Pfizer Inc. for valuable suggestions in preparation of this manuscript. As always, this idea would not be possible without teamwork and collaboration with our clinical colleagues on many drug clinical trials. Especially we would like to thank our Pfizer clinical colleague Dr Yazdi Pithavala and others for their enlightenment to accept the proposal of applying higher-order crossover designs into clinical trial practice.
The authors have no conflicts of interest that are directly relevant to the content of this study.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhou, J., Yuan, Y., Reynolds, R. et al. Cost-Efficient Higher-Order Crossover Designs in Comparative Bioavailability Studies. Clin Pharmacokinet 45, 623–632 (2006). https://doi.org/10.2165/00003088-200645060-00005
Published:
Issue Date:
DOI: https://doi.org/10.2165/00003088-200645060-00005