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

TL;DR

This paper introduces a flexible Bayesian model for recruitment forecasting in multicenter studies, allowing enrollment rates to vary over time using B-splines, improving upon the traditional constant-rate Poisson-Gamma model.

Contribution

It extends the Poisson-Gamma recruitment model by incorporating time-varying rates with B-splines, enhancing modeling flexibility for real-world recruitment patterns.

Findings

Effective in simulation studies across diverse recruitment behaviors

Successfully applied to the Canadian Co-infection Cohort data

Outperforms traditional constant-rate models in dynamic scenarios

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).