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 are 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 the Cox proportional hazards model and the accelerated failure time model. This article 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.

中文翻译：

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复杂生存数据的共享脆弱性方法：最新进展回顾


依赖性生存数据在许多情况下都会出现。一种情况是集群生存数据，其中生存数据是在家庭或医疗中心等集群上收集的。当为每个个体记录多个生存时间时，也会出现相关生存数据。脆弱性模型是处理此类数据的一种常见方法。在脆弱性模型中，依赖性以随机效应表示，称为脆弱性。脆弱性模型已与 Cox 比例风险模型和加速失效时间模型一起使用。本文回顾了各种环境下脆弱模型领域的最新进展。在每个设置中，我们提供详细的模型描述、假设、可用的估计方法和 R 包。