Heitjan D F
Center for Biostatistics and Epidemiology, Pennsylvania State University College of Medicine, Hershey 17033.
Biometrics. 1991 Jun;47(2):549-62.
The problem of accounting for the grouping of continuous, bivariate data in regression analyses is considered. Reasons why grouping must be taken seriously are advanced, and a strategy for accounting for grouping is demonstrated. The specific model asserts that, in the absence of grouping, the data would be bivariate normal. This model is used to adjust estimates of parameters in a regression relating disease severity to a grouped exposure variable, using data on pneumoconiosis in English coal miners (Ashford, 1959, Biometrics 15, 573-581). The choice of computing methods is discussed and likelihood formulas are presented.
本文考虑了在回归分析中对连续双变量数据进行分组的统计问题。文中提出了必须认真对待分组的原因，并展示了一种处理分组的策略。具体模型假定，在不存在分组的情况下，数据将呈双变量正态分布。利用英国煤矿工人尘肺病的数据（阿什福德，1959年，《生物统计学》第15卷，第573 - 581页），该模型用于调整疾病严重程度与分组暴露变量之间回归分析中的参数估计。文中讨论了计算方法的选择并给出了似然公式。
