Volodymyr Anisimov
ABSTRACT
Patient enrolment is a critical step in the conduct of clinical trials and it’s important to efficiently forecast enrolment to ensure that trials are completed on time and within budget.
We present an analytic methodology for forecasting patient enrolment performance in multicentre clinical trials. The underlying technique uses a Poisson-gamma enrolment model developed earlier by the author and co-authors. Our goal is forecasting at the interim time different characteristics of the enrolment performance on different levels, including the probability that a centre, country, or region will not recruit any patients within a given time interval, recruit not more than a given number of patients, and evaluating upper enrolment bounds for a given confidence.
To forecast centre’s performance, we use a Poisson-gamma model and interim data-driven Bayesian adjustment of the enrolment rates. To forecast country/region performance, we have developed an analytic technique using a Poisson-gamma approximation of the enrolment processes in regions by Poisson-gamma processes with aggregated parameters. This technique can be used for forecasting enrolment in regions even with a few clinical centres and is beneficial compared to a normal approximation which can be used only for rather large number of centres.
The results of the implementation in real trials demonstrate the efficiency of this methodology for dynamic real-time risk-based monitoring of the enrolment performance at various levels and equip clinical teams with the useful tools to address potential operational demands.
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Index Terms
- An Analytic Methodology for Forecasting Patient Enrolment Performance in Multicentre Clinical Trials: Forecasting Patient Enrolment Performance in Clinical Trials
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