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Modeling the relation between socioeconomic status and mortality in a mixture of majority and minority ethnic groups.

机译:对大多数少数民族混合的社会经济地位与死亡率之间的关系进行建模。

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摘要

Ethnic variation in mortality and whether this variation can be explained by socioeconomic status are of substantive interest to social epidemiologists. The authors consider the analysis of mortality data for a mixture of majority and minority ethnic groups. Such data are likely to be coarsely cross-classified by age and socioeconomic status and yet, even then, in some cells of this cross-classification the observed mortality rate will be an imprecise estimate of the underlying rate. The authors illustrate conventional and Bayesian approaches to analysis with data from the 1996 census used by the New Zealand Census-Mortality Study. A conventional approach is exploratory data analysis first followed by Poisson regression. The authors use spline smoothing within a generalized additive model framework as an exploratory data analysis, following a strategy of adding just enough model structure to gain a sensible picture. A Bayesian approach is modeling first and then a description of posterior estimates using exploratory data analysis techniques. The authors use hierarchical Poisson regression and then illustrate their posterior estimates of the mortality rate using the same spline smoothing as before. The advantage of the hierarchical Bayesian approach is that it assesses uncertainty about a Poisson regression model proposed a priori; the conventional approach assumes that the fitted Poisson regression model is correct. All analyses use software that is available at no cost.
机译:死亡率的种族差异以及这种差异是否可以通过社会经济地位来解释,这是社会流行病学家的重大兴趣。作者考虑对大多数少数民族混合在一起的死亡率数据进行分析。此类数据可能会按年龄和社会经济状况进行粗略的交叉分类,但是即使如此,在此交叉分类的某些单元中,观察到的死亡率也无法准确估算出其潜在死亡率。作者利用新西兰人口普查死亡率研究使用的1996年人口普查数据说明了传统和贝叶斯分析方法。传统方法是先进行探索性数据分析,然后进行泊松回归。作者遵循通用添加模型模型框架中的样条平滑方法,将其作为探索性数据分析,遵循的策略是添加足够的模型结构以获得清晰的图片。贝叶斯方法首先是建模,然后是使用探索性数据分析技术对后验估计进行描述。作者使用分层Poisson回归,然后使用与以前相同的样条平滑方法说明了死亡率的后验估计。多层贝叶斯方法的优点是它可以评估关于先验提出的泊松回归模型的不确定性。传统方法假设拟合的泊松回归模型是正确的。所有分析均使用免费提供的软件。

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