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Patient's recruitment is a topic of paramount interest in most medical studies. Clinical trials are usually provided with the
purpose to prove some useful properties of the treatment. The proof of concept is based on statistical considerations and
the number of patients to include (NSS) is a key point of the methodology (too large number of patients is detrimental to the
ethic � and the cost - of the trial and too small number can lead to the lack of statistical power in the analysis and impossibility
to make reliable conclusions). Much attention has been focused on the calculation of the
. However, the process of patients
recruitment and modelling of its dynamics has been very little investigating and is almost not exploited. Rojavin summaries the
situation saying �
Patient recruitment and retention remains until now more of an art rather than a science
The main question of interest is to predict the date of the end of the trial with a satisfactory precision. The usual method
is deterministic but this approach is not satisfactory because of the huge variability of the recruitment process. Thus, it is an
imperative task to develop stochastic models for patient's recruitment which will lead to the two mains applications: first, the
opportunity to evaluate the main characteristics of a clinical trial (duration, cost) and to develop tools for decision-making
process at any stage of clinical trial. Here we present the results in on the sensitivity of the so-called Poisson-Gamma model
introduced in and the extended model in which takes into account the drop-outs.
Nicolas Savy is is associated Professor in the University Paul Sabatier of Toulouse in the Team Statistics and Probability of the Toulouse Mathematics Institute. His research focuses on two ways: first theoretical problems in probability therory especially stochastic calculus, anticipative stochastic integration, L?vy processes and problems applied to medical researches especially stochastic modelling in clinical trials, survival data analysis and mediation analysis.
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