Amorim Leila D A F, Cai Jianwen
Department of Statistics, Institute of Mathematics, Federal University of Bahia, Brazil and Department of Biostatistics, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Department of Statistics, Institute of Mathematics, Federal University of Bahia, Brazil and Department of Biostatistics, School of Public Health, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
Int J Epidemiol. 2015 Feb;44(1):324-33. doi: 10.1093/ije/dyu222. Epub 2014 Dec 9.
In many biomedical studies, the event of interest can occur more than once in a participant. These events are termed recurrent events. However, the majority of analyses focus only on time to the first event, ignoring the subsequent events. Several statistical models have been proposed for analysing multiple events. In this paper we explore and illustrate several modelling techniques for analysis of recurrent time-to-event data, including conditional models for multivariate survival data (AG, PWP-TT and PWP-GT), marginal means/rates models, frailty and multi-state models. We also provide a tutorial for analysing such type of data, with three widely used statistical software programmes. Different approaches and software are illustrated using data from a bladder cancer project and from a study on lower respiratory tract infection in children in Brazil. Finally, we make recommendations for modelling strategy selection for analysis of recurrent event data.
在许多生物医学研究中,感兴趣的事件在参与者身上可能会发生不止一次。这些事件被称为复发事件。然而,大多数分析仅关注首次事件发生的时间,而忽略了后续事件。已经提出了几种统计模型来分析多个事件。在本文中,我们探索并阐述了几种用于分析复发事件时间数据的建模技术,包括多变量生存数据的条件模型(AG、PWP-TT和PWP-GT)、边际均值/率模型、脆弱性模型和多状态模型。我们还提供了一个使用三个广泛使用的统计软件程序来分析此类数据的教程。使用来自一个膀胱癌项目和巴西一项儿童下呼吸道感染研究的数据来说明不同的方法和软件。最后,我们对复发事件数据的分析建模策略选择提出建议。