Evaluating Additive Interaction Using Survival Percentiles
Evaluating Additive Interaction Using Survival Percentiles
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DOI：
10.1097/ede.0000000000000449
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发表时间：
2016-05-01
期刊：
影响因子：
5.4
通讯作者：
Orsini, Nicola
中科院分区：
文献类型：
--
作者：
Bellavia, Andrea;Bottai, Matteo;Orsini, Nicola
时间-事件分析中统计学相互作用的评价通常仅限于通过在考克斯比例风险模型中纳入乘积项来研究乘法相互作用。加性相互作用的测量是可用的，但很少使用。生存分析中的所有相互作用指标，无论是加性还是乘性，都在风险度量中，通常假设两个感兴趣的预测因子之间的相互作用在随访期间是恒定的。在生存分析中，我们引入了一个以时间为度量的加性交互作用的度量。这一指标可以通过评估生存率来计算，生存率定义为不同亚群达到相同发病率比例的时间点。使用这种方法，结果的概率是固定的，时间变量是估计的。我们还表明，通过使用一个回归模型的条件生存率的评估，包括在模型中的两个曝光之间的产品项，相互作用的评价作为一个偏差从加和性的影响。在两个二元暴露的简单情况下，乘积项被解释为生存时间的过量/减少（即，年、月、日），因为存在两种暴露。这种相互作用的测量取决于所考虑的事件的分数，从而允许评价在观察的随访期间相互作用如何变化。在生存竞争的背景下，相互作用的评价允许导出一个加性相互作用的措施，而不假设一个恒定的影响，随着时间的推移，克服了常用的方法的两个主要局限性。
Evaluation of statistical interaction in time-to-event analysis is usually limited to the study of multiplicative interaction, via inclusion of a product term in a Cox proportional-hazard model. Measures of additive interaction are available but seldom used. All measures of interaction in survival analysis, whether additive or multiplicative, are in the metric of hazard, usually assuming that the interaction between two predictors of interest is constant during the follow-up period. We introduce a measure to evaluate additive interaction in survival analysis in the metric of time. This measure can be calculated by evaluating survival percentiles, defined as the time points by which different subpopulations reach the same incidence proportion. Using this approach, the probability of the outcome is fixed and the time variable is estimated. We also show that by using a regression model for the evaluation of conditional survival percentiles, including a product term between the two exposures in the model, interaction is evaluated as a deviation from additivity of the effects. In the simple case of two binary exposures, the product term is interpreted as excess/decrease in survival time (i.e., years, months, days) due to the presence of both exposures. This measure of interaction is dependent on the fraction of events being considered, thus allowing evaluation of how interaction changes during the observed follow-up. Evaluation of interaction in the context of survival percentiles allows deriving a measure of additive interaction without assuming a constant effect over time, overcoming two main limitations of commonly used approaches.