A stochastic programming approach for clinical trial planning in new drug development

Matthew Colvin, Christos T. Maravelias

Research output: Contribution to journalArticlepeer-review

81 Scopus citations

Abstract

The paper presents a multi-stage stochastic programming formulation for the planning of clinical trials in the pharmaceutical research and development (R&D) pipeline. Scenarios are used to account for the endogenous uncertainty in clinical trial outcomes. Given a portfolio of potential drugs and limited resources, the model determines the trials to be performed in each planning period and scenario. To reduce the size of the formulation we employ a reduced set of scenarios without compromising the quality of uncertainty representation. Furthermore, we present a number of ideas that allow us to reduce the number of non-anticipativity constraints necessary to model indistinguishable scenarios. The proposed approach is the first stochastic programming formulation to address this problem.

Original languageEnglish (US)
Pages (from-to)2626-2642
Number of pages17
JournalComputers and Chemical Engineering
Volume32
Issue number11
DOIs
StatePublished - Nov 24 2008
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Keywords

  • Mixed-integer programming
  • Optimization under uncertainty
  • Pharmaceutical research and development
  • Stochastic programming

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