Simulated Clinical Trials: Principle, Good Practices, and Focus on Virtual Patients Generation

  • Nicolas Savy
  • Stéphanie Savy
  • Sandrine Andrieu
  • Sébastien Marque
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 231)

Abstract

It is a well-known fact that clinical trials is a challenging process essentially for financial, ethical, and scientific concern. For twenty years, simulated clinical trials (SCT for short) has been introduced in the drug development. It becomes more and more popular mainly due to pharmaceutical companies which aim to optimize their clinical trials (duration and expenses) and the regulatory agencies which consider simulations as an alternative tool to reduce safety issues. The whole simulation plan is based on virtual patients generation. The natural idea to do so is to perform Monte Carlo simulations from the joined distribution of the covariates. This method is named Discrete Method. This is trivial when the parameters of the distribution are known, but, in practice, data available come from historical databases. A preliminary estimation step is necessary. For Discrete Method that step may be not effective, especially when there are a lot of covariates mixing continuous and categorical ones. In this chapter, simulation studies illustrate that the so-called Continuous Method may be a good alternative to the discrete one, especially when marginal distributions are moderately bi-modal.

Keywords

Simulated clinical trials Database generation Monte Carlo simulation 

Notes

Acknowledgements

This research has received the help from IRESP during the call for proposals launched in 2012 as a part of French “Cancer Plan 2009–2013”.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Nicolas Savy
    • 1
  • Stéphanie Savy
    • 2
  • Sandrine Andrieu
    • 2
    • 3
  • Sébastien Marque
    • 4
    • 5
  1. 1.Toulouse Institute of MathematicsUniversity of Toulouse IIIToulouseFrance
  2. 2.INSERM UMR 1027University of Toulouse IIIToulouseFrance
  3. 3.Epidemiology Unit of Toulouse CHUToulouseFrance
  4. 4.CapionisParisFrance
  5. 5.OsmoseBordeauxFrance

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