A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies
Abstract
Forecasting recruitments is a key component of the monitoring phase of multicenter studies. One of the most popular techniques in this field is the Poisson-Gamma recruitment model, a Bayesian technique built on a doubly stochastic Poisson process. This approach is based on the modeling of enrollments as a Poisson process where the recruitment rates are assumed to be constant over time and to follow a common Gamma prior distribution. However, the constant-rate assumption is a restrictive limitation that is rarely appropriate for applications in real studies. In this paper, we illustrate a flexible generalization of this methodology which allows the enrollment rates to vary over time by modeling them through B-splines. We show the suitability of this approach for a wide range of recruitment behaviors in a simulation study and by estimating the recruitment progression of the Canadian Co-infection Cohort (CCC).
Cite
@article{arxiv.2301.03710,
title = {A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies},
author = {Armando Turchetta and Nicolas Savy and David A. Stephens and Erica E. M. Moodie and Marina B. Klein},
journal= {arXiv preprint arXiv:2301.03710},
year = {2023}
}