Abstract
Clinical supply is impacted by decisions and events at every stage of a clinical trial. Protocol design, logistics planning, and operational dynamics pose challenges to the management of clinical supply in terms of complexity and uncertainty. In this chapter, we propose a simulation modelling approach to address these issues and support decision-makers in effectively managing clinical supply. The approach is comprehensively described in terms of underlying structure and process, and is illustrated with adaptive trials involving dropping of arms and a Bayesian responsive-adaptive design for dose finding.
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
Abdelkafi C, Beck B, David B, Druck C, Horoho M (2009) Balancing risk and costs to optimize the clinical supply chain – a step beyond simulation. J Pharm Innov. doi:10.1007/s12247-009-9063-5
Anisimov V, Fedorov V (2007) Modelling, prediction and adaptive adjustment of recruitment in multicentre trials. Stat Med 26:4958–4975
Burnham N. Quinlan J, He W, Marshall M, Nichols N, Patel N, Parke T, Wong LB (2014) Effective drug supply for adaptive clinical trials: Recommendations by the DIA adaptive design scientific working group drug supply subteam, Therapeutic Innovation & Regulatory Science, published online 22 May 2014, doi:10.1177/2168479014530968
Byrom B (2002) Managing the medication supply chain using interactive voice response systems. Life Sci Today 3:16–18
Dowlman N, Kwak M, Wood R, Nicholls G (2006) Managing the drug supply chain with e-processes. Appl Clin Trials 15(7):40–45
McEntegart D (2003) Forced randomization: When using interactive voice response systems. Appl Clin Trials 12(10):50–58
McEntegart D, O’Gorman B (2005) The impact on supply logistics of different randomization and medication management strategies using interactive voice response systems. Pharm Eng 25(5):36–46
Nicholls G, Patel N, Byrom B (2009) Simulation: A critical tool in adaptive clinical trials. Appl Clin Trials 18(6):76–82
Orloff J, Douglas F, Pinheiro J, Levinson S, Branson M, Chaturvedi P, Ette E, Gallo P, Hisrsch G, Mehta C, Patel N, Sabir S, Springs S, Stanski D, Evers M, Fleming E, Singh N, Tramontin T, Golub H (2009) The future of drug development: advancing clinical trial design. Nat Rev Drug Discov 8(12):949–957 doi:10.1038/nrd3025
Peterson M, Byrom B, Dowlman N, McEntegart D (2004) Optimizing clinical trial supply requirements: simulation of computer-controlled supply chain management. J Soc Clin Trials 1(4):399–412
Shang K (2011) Simple heuristics for optimal inventory policies in supply chains. Chapter 7 in Tutorials in Operations Research, INFORMS, pp 106–127, doi:10.1287/educ.1110.0086
Waters S, Dowlman I, Drake K, Gamble L, Lang M, McEntegart D (2010) Enhancing control of the medication supply chain in clinical trials managed by interactive voice response systems. Drug Inf J 44(6):727–740
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer Science+Business Media New York
About this chapter
Cite this chapter
Patel, N.R., Ankolekar, S., Senchaudhuri, P. (2014). Approaches for Clinical Supply Modelling and Simulation. In: He, W., Pinheiro, J., Kuznetsova, O. (eds) Practical Considerations for Adaptive Trial Design and Implementation. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-1100-4_15
Download citation
DOI: https://doi.org/10.1007/978-1-4939-1100-4_15
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4939-1099-1
Online ISBN: 978-1-4939-1100-4
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)