Models for patients' recruitment in clinical trials and sensitivity analysis

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,...

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Bibliographic Details
Published in:Statistics in medicine 2012-07, Vol.31 (16), p.1655-1674
Main Authors: Mijoule, Guillaume, Savy, Stéphanie, Savy, Nicolas
Format: Article
Language:English
Subjects:
Bayes Theorem
Bayesian statistics
Biostatistics
Clinical trials
Clinical Trials as Topic
Humans
Mathematical models
maximum likelihood estimation
Models, Statistical
Multicenter Studies as Topic
Patient Selection
Patients
Poisson Distribution
Poisson process
recruitment time
Research Design - statistics & numerical data
Sensitivity analysis
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Summary: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 t1. Data collected on [0,t1] allow to calibrate the parameters of the model, which are then used to make predictions on what will happen after t1. 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. Copyright © 2012 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.4495