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Article overview
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Enrollment Forecast for Clinical Trials at the Portfolio Planning Phase Based on Site-Level Historical Data | | Sheng Zhong
; Yunzhao Xing
; Mengjia Yu
; Li Wang
; | | Date: |
3 Jan 2023 | | Abstract: | Accurate forecast of a clinical trial enrollment timeline at the planning
phase is of great importance to both corporate strategic planning and trial
operational excellence. While predictions of key milestones such as last
subject first dose date can inform strategic decision-making, detailed
predictive insights (e.g., median number of enrolled subjects by month for a
country) can facilitate the planning of clinical trial operation activities and
promote execution excellence. The naive approach often calculates an average
enrollment rate from historical data and generates an inaccurate prediction
based on a linear trend with the average rate. The traditional statistical
approach utilizes the simple Poisson-Gamma model that assumes time-invariant
site activation rates and it can fail to capture the underlying nonlinear
patterns (e.g., up and down site activation pattern). We present a novel
statistical approach based on generalized linear mixed-effects models and the
use of non-homogeneous Poisson processes through Bayesian framework to model
the country initiation, site activation and subject enrollment sequentially in
a systematic fashion. We validate the performance of our proposed enrollment
modeling framework based on a set of preselected 25 studies from four
therapeutic areas. Our modeling framework shows a substantial improvement in
prediction accuracy in comparison to the traditional statistical approach.
Furthermore, we show that our modeling and simulation approach calibrates the
data variability appropriately and gives correct coverage rates for prediction
intervals of various nominal levels. Finally, we demonstrate the use of our
approach to generate the predicted enrollment curves through time with
confidence bands overlaid. | | Source: | arXiv, 2301.01351 | | Services: | Forum | Review | PDF | Favorites | |
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