Soh J E, Huang Yijian
Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia.
Biometrics. 2019 Dec;75(4):1264-1275. doi: 10.1111/biom.13105. Epub 2019 Sep 12.
Recurrent events often arise in follow-up studies where a subject may experience multiple occurrences of the same event. Most regression models with recurrent events tacitly assume constant effects of covariates over time, which may not be realistic in practice. To address time-varying effects, we develop a dynamic regression model to target the mean frequency of recurrent events. We propose an estimation procedure which fully exploits observed data. Consistency and weak convergence of the proposed estimator are established. Simulation studies demonstrate that the proposed method works well, and two real data analyses are presented for illustration.
在随访研究中经常会出现复发事件，在这类研究中，一个受试者可能会多次经历同一事件。大多数处理复发事件的回归模型默认协变量的效应随时间恒定，而这在实际中可能并不现实。为了处理随时间变化的效应，我们开发了一种动态回归模型来针对复发事件的平均发生频率。我们提出了一种充分利用观测数据的估计程序。建立了所提估计量的一致性和弱收敛性。模拟研究表明所提方法效果良好，并给出了两个实际数据分析作为例证。
