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Regression models for infant mortality data in Norwegian siblings, using a compound Poisson frailty distribution with random scale

机译:挪威兄弟姐妹婴儿死亡率数据的回归模型,使用随机比例的复合泊松脆弱性分布

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The power variance function distributions, which include the gamma and compound Poisson (CP) distributions among others, are commonly used in frailty models for family data. In a previous paper, we presented a frailty model constructed by randomizing the scale parameter in a CP distribution. When combined with a parametric baseline hazard, this yields a model with heterogeneity on both the individual and the family level and a subgroup with zero frailty, corresponding to people not experiencing the event. In this paper, we discuss covariates in the model. Depending on where the covariates are inserted in the model, one may have proportional hazards at the individual level, the family level, and a larger group level (for covariates shared by many families, e.g. ethnic groups) or get accelerated failure times. Each of these alternatives gives a specific interpretation of the covariate effects. An application to data infant mortality in siblings from the Medical Birth Registry of Norway is included. We compare the results for some of the different covariate modeling options.
机译:幂方差函数分布通常包括脆弱性模型中的家庭数据,其中包括伽玛分布和复合泊松(CP)分布。在先前的论文中,我们提出了一个脆弱模型,该模型通过随机化CP分布中的比例参数来构造。当与参数基线危害相结合时,这将产生一个模型,该模型在个人和家庭水平上均具有异质性,而脆弱性为零的亚组则对应于未经历此事件的人。在本文中,我们讨论了模型中的协变量。取决于在模型中插入协变量的位置,一个人可能在个人级别,家庭级别和较大的组级别(对于许多家庭(例如,种族群体)共享的协变量)具有成比例的风险,或者会加速失败时间。这些选择中的每一个都对协变量效应给出了特定的解释。包括挪威医学出生登记处用于兄弟姐妹中婴儿死亡率数据的应用程序。我们比较了一些不同协变量建模选项的结果。

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