Given progressive developments and demands on clinical trials, accurate enrollment timeline forecasting is increasingly crucial for both strategic decision-making and trial execution excellence. Naive approach assumes flat rates on enrollment using average of historical data, while traditional statistical approach applies simple Poisson-Gamma model using timeinvariant rates for site activation and subject recruitment. Both of them are lack of nontrivial 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 is 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.

翻译：鉴于临床试验的不断进展与需求，在战略决策和试验执行卓越性方面，准确的入组时间线预测日益关键。朴素方法假设入组率恒定，采用历史数据的平均值；而传统统计方法则应用简单的泊松-伽马模型，使用时间不变的启动率和受试者招募率。这两种方法均缺乏时间、地点等关键因素。我们提出了一种新颖的两段式统计方法，基于准泊松回归进行受试者累积率建模，并利用泊松过程处理受试者入组与中心启动。输入的研究层面数据可公开获取，且能与用户所在组织的历史研究数据整合，以前瞻性地预测入组时间线。与先前工作相比，新框架简洁且准确。我们在7个精选研究上验证了所提入组模型的性能，并将其结果与其他框架进行了对比。