Whether a clinical trial is completed on schedule is a critical concern—it not only affects the management of study timelines and costs, but also significantly impacts the timing of regulatory submissions, market strategy, and post-approval revenue. Among the various phases, patient enrollment often accounts for the majority of the trial duration and is particularly difficult to predict due to its inherent uncertainty. As a result, predicting patient enrollment is both highly important and particularly challenging.

To address this challenge, statistical models have been proposed to describe and forecast enrollment dynamics. Among them, the Poisson-Gamma model, introduced by Anisimov & Fedorov (2007), is one of the most prominent. This model treats site-specific enrollment rates as gamma-distributed random variables and combines them with a Poisson process, enabling flexible modeling that accounts for heterogeneity in enrollment rates across sites. In recent years, more complex extensions of this model have also become feasible for practical use.

In practice, enrollment operations are managed by clinical operations teams, and models that fail to incorporate their insights may end up as theoretical exercises with limited practical value. At the same time, expecting operations staff to learn R or statistical modeling is unrealistic, and having statisticians accompany them for every use case is inefficient.

This presentation introduces a case study of a web-based application that enables intuitive use of enrollment prediction models without requiring R expertise. The application adopts a decoupled architecture, separating the server-side and front-end components. On the server side, the statistical model is implemented in R and exposed as an API using the plumber package, then hosted on Posit Connect. The front end, built with HTML, CSS, and JavaScript, accesses the API to retrieve model outputs and visualizations, which are then displayed in the browser. A web application removes the need for local installation and encourages real-world adoption.

The talk will primarily highlight technical challenges, architectural decisions, and implementation strategies encountered by statisticians in the pharmaceutical industry. A live demonstration of the current alpha version is also planned.
Akifumi Okayama | Motoki Oe | Nobushige Matsuoka
Pfizer | Pfizer | Pfizer
A Web-Based R Application for Forecasting Patient Enrollment in Clinical Trials
Whether a clinical trial is completed on schedule is a critical concern—it not only affects the management of study timelines and costs, but also significantly impacts the timing of regulatory submissions, market strategy, and post-approval revenue. Among the various phases, patient enrollment often accounts for the majority of the trial duration and is particularly difficult to predict due to its inherent uncertainty. As a result, predicting patient enrollment is both highly important and particularly challenging.  To address this challenge, statistical models have been proposed to describe and forecast enrollment dynamics. Among them, the Poisson-Gamma model, introduced by Anisimov & Fedorov (2007), is one of the most prominent. This model treats site-specific enrollment rates as gamma-distributed random variables and combines them with a Poisson process, enabling flexible modeling that accounts for heterogeneity in enrollment rates across sites. In recent years, more complex extensions of this model have also become feasible for practical use.  In practice, enrollment operations are managed by clinical operations teams, and models that fail to incorporate their insights may end up as theoretical exercises with limited practical value. At the same time, expecting operations staff to learn R or statistical modeling is unrealistic, and having statisticians accompany them for every use case is inefficient.  This presentation introduces a case study of a web-based application that enables intuitive use of enrollment prediction models without requiring R expertise. The application adopts a decoupled architecture, separating the server-side and front-end components. On the server side, the statistical model is implemented in R and exposed as an API using the plumber package, then hosted on Posit Connect. The front end, built with HTML, CSS, and JavaScript, accesses the API to retrieve model outputs and visualizations, which are then displayed in the browser. A web application removes the need for local installation and encourages real-world adoption.  The talk will primarily highlight technical challenges, architectural decisions, and implementation strategies encountered by statisticians in the pharmaceutical industry. A live demonstration of the current alpha version is also planned.