title: Models for patients' recruitment in clinical trials and sensitivity analysis.
abstract: Taking a decision on the feasibility and estimating the duration of patients' recruitment in a clinical trial are very important but very hard questions to answer, mainly because of the huge variability of the system. The more elaborated works on this topic are those of Anisimov and co-authors, where they investigate modelling of the enrolment period by using Gamma-Poisson processes, which allows to develop statistical tools that can help the manager of the clinical trial to answer these questions and thus help him to plan the trial. The main idea is to consider an ongoing study at an intermediate time, denoted t(1). Data collected on [0,t(1)] allow to calibrate the parameters of the model, which are then used to make predictions on what will happen after t(1). This method allows us to estimate the probability of ending the trial on time and give possible corrective actions to the trial manager especially regarding how many centres have to be open to finish on time. In this paper, we investigate a Pareto-Poisson model, which we compare with the Gamma-Poisson one. We will discuss the accuracy of the estimation of the parameters and compare the models on a set of real case data. We make the comparison on various criteria : the expected recruitment duration, the quality of fitting to the data and its sensitivity to parameter errors. We discuss the influence of the centres opening dates on the estimation of the duration. This is a very important question to deal with in the setting of our data set. In fact, these dates are not known. For this discussion, we consider a uniformly distributed approach. Finally, we study the sensitivity of the expected duration of the trial with respect to the parameters of the model : we calculate to what extent an error on the estimation of the parameters generates an error in the prediction of the duration.
authors.affiliation: University of Toulouse III, F-31073, Toulouse, France.
authors.forename: Guillaume
authors.initials: G
authors.lastname: Mijoule
authors.forename: Stéphanie
authors.initials: S
authors.lastname: Savy
authors.forename: Nicolas
authors.initials: N
authors.lastname: Savy
mesh_terms: D001499:Bayes Theorem; D002986:Clinical Trials as Topic; D006801:Humans; D015233:Models, Statistical; D015337:Multicenter Studies as Topic; D018579:Patient Selection; D016012:Poisson Distribution; D012107:Research Design / Q000706:statistics & numerical data