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首页> 外文期刊>Occupational and environmental medicine >Poisson regression analysis of ungrouped data.
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Poisson regression analysis of ungrouped data.

机译：未分组数据的Poisson回归分析。

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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.
机译：背景：泊松回归通常用于分析来自大型职业人群的流行病学数据。它通常被实现为一种数据分析的分组方法，其中对所有暴露和协变量信息进行了分类，并列出了人员时间和事件。目的：描述一种使用泊松回归分析的替代方法，该方法使用单个人时单位而不进行分组。方法：通过泊松回归分析模拟和实证研究的数据。在对模拟数据进行分析时，将没有分组的泊松回归得出的效果估计值与按比例风险回归得到的效果估计值进行了比较。对一组138 900名电气工人的经验数据进行的分析被用来说明如何在实际的职业人群分析中应用未分组的方法。结果：使用模拟数据，对未分组个人时间数据的Poisson回归分析得出的结果与通过比例风险回归获得的结果相同：两种方法的结果均给出了为模拟指定的“真实”关联的无偏估计。经验数据分析证实，当指定相同的模型时，分组和非分组分析提供的结果相同。但是，当通过Poisson回归分析估算暴露响应趋势时，可能会产生偏差，在该分析中，将暴露得分（例如类别均值或中点）分配给分组数据。结论：未分组个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分数相关的偏见，并有助于直接评估暴露分类和分数分配对回归结果的影响。

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