选择统计模型以将生物学交互评估为效应加和性的偏离
Choosing statistical models to assess biological interaction as a departure from additivity of effects
AI总结：
本文比较线性二项、对数二项和logistic模型评估二项结局中生物学交互的能力，指出线性二项模型可直接、可解释地衡量效应加和性偏离，值得推广。
摘要：
Vanderweele 和 Knol 将生物学交互定义为“两种暴露在物理上相互作用以产生结局”的情形。生物学交互的一个标志是，当各因素共同作用时产生的总效应，不同于这些因素独立作用时各效应之和。流行病学家构建统计模型来评估生物学交互。统计模型的形式决定了它适合检测效应加和性的偏离，还是适合检测效应乘积性的偏离。目前存在一种共识，即生物学交互应作为效应加和性的偏离来评估。本文比较了三种统计模型对一个数据示例的评估，该示例出现在多部流行病学教材中，用于说明二项结局中的生物学交互。线性二项模型以概率差异的形式量化该数据示例中对加和性的偏离。它可为包括交互对比（IC）在内的参数生成可直接解释的估计值和95%置信区间。对数二项回归和logistic回归模型在该数据示例中未检测到对乘积性的偏离。然而，它们的估计值有助于计算“归因于交互的相对超额风险”（RERI），这是在相对风险尺度上衡量对加和性偏离的指标。线性二项模型可直接产生对效应加和性偏离的可解释评估，值得在研究和流行病学教学中更广泛使用。目前已有策略可用于解决该模型的局限性。
英文摘要：
Vanderweele and Knol define biological interaction as an instance wherein "two exposures physically interact to bring about the outcome." A hallmark of biological interaction is that the total effect, produced when factors act together, differs from the sum of effects when the factors operate independently. Epidemiologists construct statistical models to assess biological interaction. The form of the statistical model determines whether it is suited to detecting departures from additivity of effects or for detecting departures from multiplicativity of effects. A consensus exists that biological interaction should be assessed as a departure from additivity of effects. This paper compares three statistical models' assessment of a data example that appears in several epidemiology textbooks to illustrate biological interaction in a binomial outcome. A linear binomial model quantifies departure from additivity in the data example in terms of differences in probabilities. It generates directly interpretable estimates and 95% confidence intervals for parameters including the interaction contrast (IC). Log binomial and logistic regression models detect no departure from multiplicativity in the data example. However, their estimates contribute to calculation of a "Relative Excess Risk Due to Interaction" (RERI), a measure of departure from additivity on a relative risk scale. The linear binomial model directly produces interpretable assessments of departures from additivity of effects and deserves wider use in research and in the teaching of epidemiology. Strategies exist to address the model's limitations.
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