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A forwards induction approach to candidate drug selection

Part of: Operations research and management science Mathematical economics

Published online by Cambridge University Press:  01 July 2016

S. Qu  and
J. C. Gittins
S. Qu*
Affiliation:
University of Oxford
J. C. Gittins*
Affiliation:
University of Oxford
*
∗ Postal address: Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
∗ Postal address: Department of Statistics, University of Oxford, 1 South Parks Road, Oxford OX1 3TG, UK.
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Abstract

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A forwards induction policy is a type of greedy algorithm for Markov decision processes. It is straightforward to implement and is optimal for a large class of models, especially in stochastic resource allocation. In this paper we consider a model for the optimal allocation of resources in pre-clinical pharmaceutical research. We show that although they are not always strictly optimal, forwards induction policies perform well.

Keywords

Pharmaceutical researchresource allocationforwards inductionMarkov decision process

MSC classification

Primary: 90B36: Scheduling theory, stochastic
Secondary: 90C40: Markov and semi-Markov decision processes 91B32: Resource and cost allocation
Type
General Applied Probability
Information
Advances in Applied Probability , Volume 43 , Issue 3 , September 2011 , pp. 649 - 665
Copyright
Copyright © Applied Probability Trust 2011 

References

Brealey, R. and Myers, S. (2000). Principles of Corporate Finance, 6th edn. McGraw Hill, New York.Google Scholar
Casella, G. and Berger, R. L. (2001). Statistical Inference, 2nd edition. Duxbury Press.Google Scholar
Charalambous, C. (2009). Models and software for improving the profitability of pharmaceutical research. , University of Oxford.Google Scholar
Charalambous, C. and Gittins, J. C. (2008). Optimal selection policies for a sequence of candidate drugs. Adv. Appl. Prob. 40, 359–376.Google Scholar
Gittins, J. C. (1979). Bandit processes and dynamic allocation indices. J. R. Statist. Soc. B 41, 148–177.Google Scholar
Gittins, J., Glazebrook, K. and Weber, R. (2011a). Multi-Armed Bandit Allocation Indices, 2nd edn. John Wiley, Chichester.Google Scholar
Gittins, J. C. et al. (2011b). OPRRA User Guide. Available at www.stats.ox.ac.uk/people/academic_staff/john_gittins.Google Scholar
Glazebrook, K. D. (1995). Stochastic scheduling and forwards induction. Discrete Appl. Math. 57, 145–165.CrossRefGoogle Scholar
Glazebrook, K. D. and Gittins, J. C. (1993). The performance of forwards induction policies. Stoch. Process. Appl. 46, 301–326.Google Scholar
Press, W. H., Teukolsky, S. A., Vetterling, W. T. and Flannery, B. P. (2007). Numerical Recipes, 3rd edn. Cambridge University Press.Google Scholar
Puterman, M. L. (2005). Markov Decision Processes, 2nd edn. John Wiley, New York.Google Scholar
Yu, J. Y. and Gittins, J. C. (2008). Models and software for improving the profitability of pharmaceutical research. Europ. J. Operat. Res. 189, 459–475.Google Scholar