Section of Social Medicine, Department of Public Health, University of Copenhagen, Copenhagen, Denmark.
Epidemiology. 2012 Sep;23(5):733-7. doi: 10.1097/EDE.0b013e31825fa218.
It is a widely held belief in public health and clinical decision-making that interventions or preventive strategies should be aimed at patients or population subgroups where most cases could potentially be prevented. To identify such subgroups, deviation from additivity of absolute effects is the relevant measure of interest. Multiplicative survival models, such as the Cox proportional hazards model, are often used to estimate the association between exposure and risk of disease in prospective studies. In Cox models, deviations from additivity have usually been assessed by surrogate measures of additive interaction derived from multiplicative models-an approach that is both counter-intuitive and sometimes invalid. This paper presents a straightforward and intuitive way of assessing deviation from additivity of effects in survival analysis by use of the additive hazards model. The model directly estimates the absolute size of the deviation from additivity and provides confidence intervals. In addition, the model can accommodate both continuous and categorical exposures and models both exposures and potential confounders on the same underlying scale. To illustrate the approach, we present an empirical example of interaction between education and smoking on risk of lung cancer. We argue that deviations from additivity of effects are important for public health interventions and clinical decision-making, and such estimations should be encouraged in prospective studies on health. A detailed implementation guide of the additive hazards model is provided in the appendix.
在公共卫生和临床决策中，人们普遍认为干预措施或预防策略应该针对那些大多数病例可能得到预防的患者或人群亚组。为了识别这些亚组，偏离绝对效应的可加性是相关的感兴趣的度量。多变量生存模型，如 Cox 比例风险模型，常用于前瞻性研究中估计暴露与疾病风险之间的关联。在 Cox 模型中，偏离可加性通常通过从乘法模型中得出的添加剂相互作用的替代测量来评估——这种方法既违背直觉，有时又无效。本文通过使用加法危害模型，提出了一种直接而直观的方法来评估生存分析中效应的可加性偏差。该模型直接估计偏离可加性的绝对大小，并提供置信区间。此外，该模型可以同时适应连续和分类暴露，并在相同的潜在尺度上对暴露和潜在混杂因素进行建模。为了说明这种方法，我们提出了一个教育和吸烟对肺癌风险相互作用的实证例子。我们认为，效应的可加性偏差对于公共卫生干预和临床决策很重要，应该鼓励在健康的前瞻性研究中进行这样的估计。加法危害模型的详细实施指南见附录。
