Fitting Parametric Counting Processes by Using Log-Linear Models
本文展示了如何用标准对数线性回归模型来拟合参数计数过程，通过两个实例（包括慢性肉芽肿病感染数据）说明该方法能用于选择不同复杂度的模型，并揭示过去事件对重复事件风险的影响。
Counting processes constitute a means of describing how and when a series of events occurs to individuals. The risk or intensity of events, which may vary over time, can depend on any aspects of the previous history of the individual. Standard log-linear regression modelling techniques are used to choose from the explanatory variables those which are appropriate to describe this dependence on the past. Details are given on how to set up such repeated measurements of duration among events as log-linear models. Two examples show how the technique can be used, even for simple survival data, to choose between models of different complexity and highlight the importance of dependence on the past for repeated events such as infection due to chronic granulotomous disease in the study of the effect of gamma interferon treatment.