Abstract：
Given progressive developments and demands on clinical trials, accurate enrollment timeline forecasting is increasingly crucial for both strategic decision-making and trial execution excellence. Naïve approach assumes flat rates on enrollment using average of historical data, while traditional statistical approach applies simple Poisson-Gamma model using time-invariant rates for site activation and subject recruitment. Both of them are lack of non-trivial factors such as time and location. We propose a novel two-segment statistical approach based on Quasi-Poisson regression for subject accrual rate and Poisson process for subject enrollment and site activation. The input study-level data are publicly accessible and it can be integrated with historical study data from user\'s organization to prospectively predict enrollment timeline. The new framework is neat and accurate compared to preceding works. We validate the performance of our proposed enrollment model and compare the results with other frameworks on 7 curated studies.
摘要：
鉴于临床试验的进展和需求，准确的注册时间表预测对于战略决策和卓越的试用执行越来越重要。天真方法假设使用历史数据的平均值进行统一的入学率，而传统的统计方法应用简单的Poisson-Gamma模型，使用时不变率进行位点激活和受试者招募。两者都缺乏时间和地点等非平凡因素。我们提出了一种新颖的基于准泊松回归的两段统计方法，用于受试者的应计率和泊松过程，用于受试者的注册和站点激活。输入的研究级别数据可公开访问，并且可以与用户组织的历史研究数据集成，以前瞻性地预测注册时间表。与之前的作品相比，新框架整洁准确。我们验证了我们提出的注册模型的性能，并将结果与7项精选研究的其他框架进行了比较。
