Performances of Poisson–Gamma Model for Patients’ Recruitment in Clinical Trials When There Are Pauses in Recruitment or When the Number of Centres is Small

  • Nathan Minois
  • Guillaume Mijoule
  • Stéphanie Savy
  • Valérie Lauwers-Cances
  • Sandrine Andrieu
  • Nicolas Savy
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 231)


To predict the duration of a clinical trial is a question of paramount interest. To date, the more elaborated model is the so-called Poisson–gamma model introduced by Anisimov and Fedorov in 2007. Theoretical performances of this model are asymptotic and have been established under assumptions especially on the recruitment rates by centre which are assumed to be constant in time. In order to evaluate the practical use of this model, ranges of validity have to be assessed. By means of simulation studies, authors investigate, on the one hand, the impact of the number of centres involved, of the average recruitment rate, of the duration of recruitment and of the interim time of analysis on the expected duration of the trial and, on the other hand, two strategies of estimation of the trial duration accounting for breaks in recruitment (period during which centres do not recruit) which are compared and discussed. These investigations yield to guidelines on the use of Poisson–gamma processes to model recruitment dynamics regarding these issues.


Clinical trials Recruitment time Bayesian statistics Cox processes 



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

  • Nathan Minois
    • 1
  • Guillaume Mijoule
    • 2
  • Stéphanie Savy
    • 1
  • Valérie Lauwers-Cances
    • 3
  • Sandrine Andrieu
    • 1
    • 4
  • Nicolas Savy
    • 5
  1. 1.INSERM UMR 1027University of Toulouse IIIToulouseFrance
  2. 2.University of Paris XIOrsayFrance
  3. 3.Epidemiology UnitCHU PurpanToulouseFrance
  4. 4.Epidemiology Unit of Toulouse CHUToulouseFrance
  5. 5.Toulouse Institute of MathematicsUniversity of Toulouse IIIToulouseFrance

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