Su Chien-Lin, Steele Russell J, Shrier Ian
Department of Mathematics and Statistics, McGill University, Montréal, QC, Canada.
Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
Lifetime Data Anal. 2021 Jul;27(3):357-387. doi: 10.1007/s10985-021-09519-3. Epub 2021 Mar 25.
Recurrent event data arise in many biomedical longitudinal studies when health-related events can occur repeatedly for each subject during the follow-up time. In this article, we examine the gap times between recurrent events. We propose a new semiparametric accelerated gap time model based on the trend-renewal process which contains trend and renewal components that allow for the intensity function to vary between successive events. We use the Buckley-James imputation approach to deal with censored transformed gap times. The proposed estimators are shown to be consistent and asymptotically normal. Model diagnostic plots of residuals and a method for predicting number of recurrent events given specified covariates and follow-up time are also presented. Simulation studies are conducted to assess finite sample performance of the proposed method. The proposed technique is demonstrated through an application to two real data sets.
在许多生物医学纵向研究中，当与健康相关的事件在随访期间每个受试者都可能反复发生时，就会出现复发事件数据。在本文中，我们研究了复发事件之间的间隔时间。我们基于趋势更新过程提出了一种新的半参数加速间隔时间模型，该模型包含趋势和更新成分，允许强度函数在连续事件之间变化。我们使用Buckley-James插补方法来处理截尾变换后的间隔时间。所提出的估计量被证明是一致的且渐近正态的。还给出了残差的模型诊断图以及在给定特定协变量和随访时间的情况下预测复发事件数量的方法。进行了模拟研究以评估所提方法的有限样本性能。通过应用于两个真实数据集展示了所提技术。
