A time-dependent Poisson-Gamma model for recruitment forecasting in multicenter studies

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.

Automated summary

Forecasting recruitments is crucial in the monitoring phase of multicenter studies. The Poisson-Gamma recruitment model is a popular technique based on the doubly stochastic Poisson process. However, the assumption of constant recruitment rates is often unrealistic in real studies. This paper presents a flexible generalization of the model, allowing varying enrollment rates over time using B-splines. The approach is shown to be suitable for a wide range of recruitment behaviors in simulations and is applied to estimate recruitment progression in a Canadian Co-infection Cohort.