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.
翻译:规划阶段临床试用入学时间表的准确预测对于公司战略规划和实验性业务优异都非常重要。虽然对最后一个主题第一剂量日期等关键里程碑的预测可以为战略决策提供依据,但详细的预测见解(例如,一个国家每月注册学生的中位数)可便利临床试验业务活动的规划,并促进执行优异。天真的方法往往从历史数据中计算平均入学率,并根据平均比率的线性趋势产生不准确的预测。传统的统计方法利用简单的Poisson-Gamma模型,该模型假设时间变化点启动率,它可能无法捕捉基本的非线性模式(例如,上下站点启动模式),但详细的预测见解(例如,一个国家每月注册学生的中位数)可促进临床试验活动规划,并促进执行优异的优异做法。天真的方法往往从历史数据中计算出平均入学率,并根据平均比率的线性趋势产生不准确的预测。我们根据一套预选的25项研究从4个治疗地区启动率和下调率方法来验证我们拟议的入学模型的绩效框架的绩效,我们用各种模型来进行精确的预测。我们最后的模型显示统计的准确性数据,我们通过统计的精确的预测方法来显示我们的精确度。我们的数据的精确度,我们通过模型来显示我们的统计的精确度。