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Monte Carlo Simulation for Trial Design Tool

In: Principles and Practice of Clinical Trials

Author

Listed:
  • Suresh Ankolekar

    (Cytel Inc
    Maastricht School of Management)

  • Cyrus Mehta

    (Cytel Inc
    Harvard T.H. Chan School of Public Health)

  • Rajat Mukherjee

    (Cytel Inc)

  • Sam Hsiao

    (Cytel Inc)

  • Jennifer Smith

    (Sunesis Pharmaceuticals Inc)

  • Tarek Haddad

    (Medtronic Inc)

Abstract

Clinical trials often involve design issues with mathematically intractable complexity. Being part of multi-phase drug development programs, the trial designs need to incorporate prior information in terms of historical data from earlier phases and available knowledge about related trials. Some trials with inherent limits on data collection may need augmentation with simulated pseudo-data. For planning of interim looks, group sequential and adaptive trials require accurate timeline predictions of reaching clinical milestones involving complex set of operational and clinical models. In general, clinical trial design involves an interactive process involving interplay of models, data, assumptions, insights, and experiences to address specific design issues before and during the trial. This offers a rich context for simulation-centric modeling, the theme of this chapter. We will focus on practical considerations of applying simulation modeling tools and techniques to design and implementation of clinical trials. This will be achieved through two real-life case studies and relevant illustrative examples drawn from literature and our practical experience.

Suggested Citation

  • Suresh Ankolekar & Cyrus Mehta & Rajat Mukherjee & Sam Hsiao & Jennifer Smith & Tarek Haddad, 2022. "Monte Carlo Simulation for Trial Design Tool," Springer Books, in: Steven Piantadosi & Curtis L. Meinert (ed.), Principles and Practice of Clinical Trials, chapter 80, pages 1563-1585, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-52636-2_251
    DOI: 10.1007/978-3-319-52636-2_251
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