Dependent survival data arise in many contexts. One context is clustered survival data, where survival data are collected on clusters such as families or medical centers. Dependent survival data also arise when multiple survival times are recorded for each individual. Frailty models is one common approach to handle such data. In frailty models, the dependence is expressed in terms of a random effect, called the frailty. Frailty models have been used with both Cox proportional hazards model and the accelerated failure time model. This paper reviews recent developments in the area of frailty models in a variety of settings. In each setting we provide a detailed model description, assumptions, available estimation methods, and R packages.
翻译:多种情况下产生了依赖性生存数据。一种情况是生存数据分组,收集了家庭或医疗中心等群组的生存数据。在记录每个人的多个生存时间时,也会出现依赖性生存数据。脆弱模型是处理这类数据的一种常见方法。在脆弱模型中,依赖性以随机效应表示,称为脆弱。脆弱模型用于Cox比例危害模型和加速故障时间模型。本文回顾了脆弱模型领域在各种环境下的最新动态。在每种环境中,我们提供了详细的模型描述、假设、可用估算方法和R包。