Poisson regression analysis of ungrouped data
Poisson regression analysis of ungrouped data
复制标题
DOI：
10.1136/oem.2004.017459
复制
发表时间：
2005-05-01
影响因子：
4.9
通讯作者：
Elliott, L
中科院分区：
文献类型：
--
作者：
Loomis, D;Richardson, DB;Elliott, L
背景：泊松回归通常用于分析大型职业队列研究的流行病学数据。它通常被实现为一个分组的数据分析方法，其中所有的暴露和协变量信息进行分类和人的时间和事件列表aims：To描述一种替代方法泊松回归分析使用单个单位的人的时间没有grouping.Methods：模拟和经验队列的数据进行了分析泊松回归。在模拟数据的分析中，将通过泊松回归（未分组）获得的效应估计值与比例风险回归下获得的效应估计值进行比较。一个队列的138 900名电力工人的经验数据分析被用来说明如何未分组的方法可以应用在实际的职业cohols.Results的分析：使用模拟数据，泊松回归分析未分组的人的时间数据产生的结果相当于通过比例风险回归获得的结果：两种方法的结果给出了模拟所指定的"真实"关联的无偏估计。经验数据的分析证实，当指定相同的模型时，分组和未分组的分析提供相同的结果。然而，当通过Poisson回归分析估计暴露-反应趋势时，可能会出现偏倚，在Poisson回归分析中，暴露评分（如类别均值或中点）被分配给分组数据。未分组的人-时间数据的泊松回归分析是一种有用的工具，可以避免与分类暴露数据和分配暴露评分相关的偏倚，并便于直接评估暴露分类和评分分配对回归结果的影响。
Background: Poisson regression is routinely used for analysis of epidemiological data from studies of large occupational cohorts. It is typically implemented as a grouped method of data analysis in which all exposure and covariate information is categorised and person-time and events are tabulated.Aims: To describe an alternative approach to Poisson regression analysis using single units of person-time without grouping.Methods: Data for simulated and empirical cohorts were analysed by Poisson regression. In analyses of simulated data, effect estimates derived via Poisson regression without grouping were compared to those obtained under proportional hazards regression. Analyses of empirical data for a cohort of 138 900 electrical workers were used to illustrate how the ungrouped approach may be applied in analyses of actual occupational cohorts.Results: Using simulated data, Poisson regression analyses of ungrouped person-time data yield results equivalent to those obtained via proportional hazards regression: the results of both methods gave unbiased estimates of the "true'' association specified for the simulation. Analyses of empirical data confirm that grouped and ungrouped analyses provide identical results when the same models are specified. However, bias may arise when exposure-response trends are estimated via Poisson regression analyses in which exposure scores, such as category means or midpoints, are assigned to grouped data.Conclusions: Poisson regression analysis of ungrouped person-time data is a useful tool that can avoid bias associated with categorising exposure data and assigning exposure scores, and facilitate direct assessment of the consequences of exposure categorisation and score assignment on regression results.