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首页> 外文期刊>Journal of applied statistics >Analyzing dependence in incidence of diabetes and heart problem using generalized bivariate geometric models with covariates
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Analyzing dependence in incidence of diabetes and heart problem using generalized bivariate geometric models with covariates

机译：使用带有协变量的广义双变量几何模型分析糖尿病和心脏问题的发病率依赖性

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摘要

For analyzing incidence data on diabetes and health problems, the bivariate geometric probability distribution is a natural choice but remained unexplored largely due to lack of models linking covariates with the probabilities of bivariate incidence of correlated outcomes. In this paper, bivariate geometric models are proposed for two correlated incidence outcomes. The extended generalized linear models are developed to take into account covariate dependence of the bivariate probabilities of correlated incidence outcomes for diabetes and heart diseases for the elderly population. The estimation and test procedures are illustrated using the Health and Retirement Study data. Two models are shown in this paper, one based on conditional-marginal approach and the other one based on the joint probability distribution with an association parameter. The joint model with association parameter appears to be a very good choice for analyzing the covariate dependence of the joint incidence of diabetes and heart diseases. Bootstrapping is performed to measure the accuracy of estimates and the results indicate very small bias.
机译：为了分析有关糖尿病和健康问题的发病率数据，双变量几何概率分布是一种自然选择，但由于缺乏将协变量与相关结果的双变量发生概率联系起来的模型，因此仍未进行充分探讨。在本文中，针对两个相关的发病结果提出了双变量几何模型。开发扩展的广义线性模型时，要考虑到老年人与糖尿病和心脏病相关联的双变量概率对相关变量的相关性。使用健康和退休研究数据说明了估算和测试程序。本文展示了两种模型，一种基于条件边际方法，另一种基于具有关联参数的联合概率分布。具有关联参数的关节模型似乎是分析糖尿病和心脏病关节发病率的协变量依赖性的很好选择。进行自举以测量估计的准确性，结果表明偏差很小。

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