关于建模多元情形I区间删失数据的Addams族离散脆弱性分布
On the Addams family of discrete frailty distributions for modelling multivariate case I interval-censored data
AI总结：
本文研究了Addams族离散脆弱性分布的实现与解释，针对多元情形I区间删失数据提出了共享脆弱性模型下的参数估计方法，并利用其支撑集依赖于参数的特性实现了非齐次协变量效应建模。
摘要：
事件时间数据的随机效应模型，也称为脆弱性模型，提供了一种概念上引人入胜的方法来量化生存时间之间的关联性，并表征由难以或无法测量的因素导致的异质性。在文献中，通常假设随机效应服从连续分布。然而，在某些应用领域，离散脆弱性分布可能更为合适。本文旨在探讨Addams族离散脆弱性分布的实现与解释。针对情形I区间删失数据的风险率，我们在共享脆弱性模型的背景下，提出了该密度族的参数估计方法。我们的优化框架允许通过协变量对随机效应分布进行分层。我们强调了Addams族离散脆弱性分布与K-point分布相较于其他脆弱性分布在解释上的优势。Addams族与K-point分布的独特之处在于，脆弱性分布的支撑集依赖于其参数。这一特征的最佳利用方式是对分布参数施加模型，从而生成一个具有非齐次协变量效应的模型，该模型可以使用风险比等标准测度进行分析。我们的方法通过应用于多元情形I区间删失感染数据得到了说明。
英文摘要：
Random effect models for time-to-event data, also known as frailty models, provide a conceptually appealing way of quantifying association between survival times and of representing heterogeneities resulting from factors which may be difficult or impossible to measure. In the literature, the random effect is usually assumed to have a continuous distribution. However, in some areas of application, discrete frailty distributions may be more appropriate. The present paper is about the implementation and interpretation of the Addams family of discrete frailty distributions. We propose methods of estimation for this family of densities in the context of shared frailty models for the hazard rates for case I interval-censored data. Our optimization framework allows for stratification of random effect distributions by covariates. We highlight interpretational advantages of the Addams family of discrete frailty distributions and the K-point distribution as compared to other frailty distributions. A unique feature of the Addams family and the K-point distribution is that the support of the frailty distribution depends on its parameters. This feature is best exploited by imposing a model on the distributional parameters, resulting in a model with non-homogeneous covariate effects that can be analysed using standard measures such as the hazard ratio. Our methods are illustrated with applications to multivariate case I interval-censored infection data.
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