Cook R J, Lawless J F
Department of Statistics and Actuarial Science, University of Waterloo, Canada.
Stat Med. 1997 Apr 30;16(8):911-24. doi: 10.1002/(sici)1097-0258(19970430)16:8<911::aid-sim544>3.0.co;2-i.
Chronic medical conditions are often manifested by the incidence of recurrent adverse clinical events. In clinical trials designed to investigate therapeutic interventions for such conditions it is natural to make treatment comparisons on the basis of event occurrence. However, when there is a more serious, possibly related, event that terminates the occurrence of the recurrent events, the problem of dependent censoring arises. Here, we consider robust modelling strategies for expressing covariate effects on the recurrent event process that address the possible dependence between the recurrent and terminal events. The various methods differ in the way the dependence is addressed, and hence in the interpretation of covariate effects. The methods are applied to a data set from a kidney transplant study and simulated data chosen for illustrative purposes.
慢性疾病通常表现为反复出现不良临床事件。在旨在研究此类疾病治疗干预措施的临床试验中，基于事件发生情况进行治疗比较是很自然的。然而，当出现更严重的、可能相关的事件导致反复事件的发生终止时，就会出现相依删失问题。在此，我们考虑用于表达协变量对反复事件过程影响的稳健建模策略，该策略能解决反复事件和终末事件之间可能存在的相依性。各种方法在处理相依性的方式上有所不同，因此在协变量效应的解释上也有所不同。这些方法应用于一项肾移植研究的数据集以及为说明目的而选择的模拟数据。
