An Integrated Framework Predicting Enrollment and Interim Analysis Timing in Clinical Trials with Recurrent Count Outcomes
Year: 2026
Type: article
Abstract: Accurate prediction of enrollment and interim analysis (IA) timing is critical for the effective management of clinical trials employing group sequential designs. Traditional forecasting methods often focus on trials with independent time‐to‐event outcomes but no effective statistical methods have been developed for trials involving recurrent count data, such as vaso‐occlusive crisis counts in sickle cell disease or number of bleeding events in hemophilia. This manuscript introduces a novel integrated framework designed to predict both future enrollment and recurrent count outcomes in group sequential trials in the setting of a randomized two‐arm comparative trial. The framework integrates existing methodologies for count data and enrollment predictions with new approaches tailored to the recurrent nature of outcomes, aiming to enhance prediction accuracy and reliability. Specifically, we develop a prediction model based on a negative binomial distribution to handle the overdispersed count data and incorporate enrollment projections. We validate our framework through extensive numerical simulations and apply it to a real phase 3 study in sickle cell disease. The results demonstrate that the proposed method has desirable performance in the prediction for future enrollment time and timing of IA defined by the information fraction (IF) with high accuracy and reduced bias. The findings underscore the framework’s potential to improve trial management by providing more accurate and reliable IA timing prediction and facilitating better decision‐making in clinical trials with recurrent count outcomes.
Source: Figshare
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7162575280