Abstract
Bayesian approach has been increasingly applied to various aspects of design and analysis of clinical trials. We present one application concerning an interim futility analysis of a trial. Longitudinal data were collected for a range of the studied doses. Bayesian analysis was first conducted to predict observations at the end of treatment for patients not yet followed through treatment, based on all interim observed data. The predicted data in combination with observed data at the end of treatment were then analyzed using a Bayesian normal dynamic linear model for dose response inference. Summary of the Bayesian analysis was used to aid an interim futility decision.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bornkamp, B., Bretz, F., Dmitrienko, A., Enas, G., Gaydos, B., Hsu, C.-H., et al. (2007). Innovative approaches for designing and analyzing adaptive dose-ranging trials. Journal of Biopharmaceutical Statistics, 17, 965–995.
Chuang-Stein, C. (2006). Sample size and the probability of a successful trial. Pharmaceutical Statistics, 5, 305–309.
Dmitrienko, A., & Wang, M.-D. (2006). Bayesian predictive approach to interim monitoring in clinical trials. Statistics in Medicine, 25, 2178–2195.
Fu, H., & Manner, D. (2010). Bayesian adaptive dose-finding studies with delayed responses. Journal of Biopharmaceutical Statistics, 20, 1055–1070.
Krams, M., Lees, K. R., Hacke, W., Grieve, A. P., Orgogozo, J. M., & Ford, G. A. for the ASTIN Study investigators (2003). ASTIN: An adaptive dose-response study of UK- 279,276 in acute ischemic stroke. Stroke, 34, 2543–2548.
Smith, M. K., Jones, I., Morris, M. F., Grieve, A. P., & Tan K. (2006). Implementation of a Bayesian adaptive design in a proof of concept study. Pharmaceutical Statistics, 5, 39–50.
Thomas, N., Sweeney, K., & Somayaji, V. (2014). Meta-analysis of clinical dose response in a large drug development portfolio. Statistics in Biopharmaceutical Research, 6, 302–317.
Wang, M.-D. (2015). Applications of probability of study success in clinical drug development. In: Z. Chen, A. Liu, Y. Qu, L. Tang, N. Ting, & Y. Tsong (Eds.). Applied Statistics in Biomedicine and Clinical Trial Designs: Selected Papers from 2013 ICSA/ISBS Joint Statistical Meetings (pp. 185–196). Berlin: Springer.
Wang, M.-D., & Li, G. Y. (2013). Bayesian interim inference of probability of clinical trial success. In: M. Hu, Y. Liu, & J. Lin (Eds.) Topics in Applied Statistics: 2012 Symposium of International Chinese Statistical Association - Springer Proceedings in Mathematics and Statistics (Vol. 55, pp. 137–147). New York: Springer.
West, M., & Harrison, P. J. (1997). Bayesian forecasting and dynamic models (2nd ed.). New York: Springer.
Acknowledgements
The authors thank Karen Price of Eli Lilly and Company and two referees for their reviews and helpful comments.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Wang, MD., Williams, D.A., Gomez, E.V., Rayamajhi, J.N. (2016). Bayesian Modeling of Time Response and Dose Response for Predictive Interim Analysis of a Clinical Trial. In: Jin, Z., Liu, M., Luo, X. (eds) New Developments in Statistical Modeling, Inference and Application. ICSA Book Series in Statistics. Springer, Cham. https://doi.org/10.1007/978-3-319-42571-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-42571-9_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-42570-2
Online ISBN: 978-3-319-42571-9
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)