Modelling and forecasting patient recruitment in clinical trials with patients' dropout

TL;DR

This paper develops statistical models to accurately forecast patient recruitment in clinical trials by incorporating dropout rates, using a Poisson-gamma framework and dropout models, with validation through simulations.

Contribution

It introduces new dropout models and estimation techniques within the Poisson-gamma recruitment framework for better forecasting accuracy.

Findings

Simulation confirms the model's applicability.

Accounting for dropout improves recruitment forecasts.

Dropout modeling is essential for trial planning.

Abstract

This paper focuses on statistical modelling and prediction of patient recruitment in clinical trials accounting for patients dropout. The recruitment model is based on a Poisson-gamma model introduced by Anisimov and Fedorov (2007), where the patients arrive at different centres according to Poisson processes with rates viewed as gamma-distributed random variables. Each patient can drop the study during some screening period. Managing the dropout process is of a major importance but data related to dropout are rarely correctly collected. In this paper, a few models of dropout are proposed. The technique for estimating parameters and predicting the number of recruited patients over time and the recruitment time is developed. Simulation results confirm the applicability of the technique and thus, the necessity to account for patients dropout at the stage of forecasting recruitment in clinical trials.