Lim Hyun J, Zhang Xu
Department of Community Health and Epidemiology, College of Medicine, University of Saskatchewan, 107 Wiggins Road, Saskatoon, SK S7N 5E5, Canada.
Accid Anal Prev. 2009 Mar;41(2):211-6. doi: 10.1016/j.aap.2008.07.015. Epub 2008 Oct 10.
In survival analysis, the Cox model is a multiplicative model and widely used in survival analysis. However, the assumption of proportional hazards in the Cox multiplicative model is a crucial one that needs to be fulfilled for the results to be meaningful. When proportionality is a questionable assumption, an alternative but less widely used method is additive model. The additive hazards model assumes that covariates act in an additive manner on an unknown baseline hazard rate. Using the emergency department (ED) visits data, we demonstrated the additive hazards regression models and showed the differences in estimates obtained by the additive hazards models and the Cox model. In our study, the Cox model gave a higher estimate than the additive hazards model. However, both models revealed similar results with regard to covariates selected to remain in the model and the estimated survival functions based on the Cox and additive hazards models were almost identical. Since Cox and additive hazards models give different aspects of the association between risk factors and the study outcome, it seems desirable to use together to give a more comprehensive understanding of data.
在生存分析中，Cox模型是一种乘法模型，在生存分析中被广泛使用。然而，Cox乘法模型中的比例风险假设是一个关键假设，只有满足该假设，结果才有意义。当比例性是一个有疑问的假设时，一种替代但使用不太广泛的方法是加法模型。加法风险模型假设协变量以加法方式作用于未知的基线风险率。使用急诊科就诊数据，我们展示了加法风险回归模型，并展示了加法风险模型和Cox模型获得的估计值之间的差异。在我们的研究中，Cox模型给出的估计值高于加法风险模型。然而，在选择保留在模型中的协变量方面，两个模型都显示出相似的结果，并且基于Cox模型和加法风险模型的估计生存函数几乎相同。由于Cox模型和加法风险模型给出了风险因素与研究结果之间关联的不同方面，因此一起使用似乎有助于更全面地理解数据。
