Deng Yi, Zhang Xiaoxi, Long Qi
1 Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, USA.
2 Pfizer Inc., New York, NY, USA.
Stat Methods Med Res. 2017 Apr;26(2):752-765. doi: 10.1177/0962280214557581. Epub 2014 Nov 3.
In multi-regional trials, the underlying overall and region-specific accrual rates often do not hold constant over time and different regions could have different start-up times, which combined with initial jump in accrual within each region often leads to a discontinuous overall accrual rate, and these issues associated with multi-regional trials have not been adequately investigated. In this paper, we clarify the implication of the multi-regional nature on modeling and prediction of accrual in clinical trials and investigate a Bayesian approach for accrual modeling and prediction, which models region-specific accrual using a nonhomogeneous Poisson process and allows the underlying Poisson rate in each region to vary over time. The proposed approach can accommodate staggered start-up times and different initial accrual rates across regions/centers. Our numerical studies show that the proposed method improves accuracy and precision of accrual prediction compared to existing methods including the nonhomogeneous Poisson process model that does not model region-specific accrual.
在多区域试验中,潜在的总体和特定区域的入组率通常不会随时间保持恒定,并且不同区域可能有不同的启动时间,这与每个区域内入组的初始跃升相结合,常常导致总体入组率不连续,而与多区域试验相关的这些问题尚未得到充分研究。在本文中,我们阐明了多区域性质对临床试验中入组建模和预测的影响,并研究了一种用于入组建模和预测的贝叶斯方法,该方法使用非齐次泊松过程对特定区域的入组进行建模,并允许每个区域的潜在泊松率随时间变化。所提出的方法可以适应不同区域/中心交错的启动时间和不同的初始入组率。我们的数值研究表明,与包括未对特定区域入组进行建模的非齐次泊松过程模型在内的现有方法相比,所提出的方法提高了入组预测的准确性和精度。