Department of Community Health & Epidemiology College of Medicine, University of Saskatchewan, Saskatoon, Canada.
BMC Med Res Methodol. 2011 Jun 27;11:101. doi: 10.1186/1471-2288-11-101.
Sequentially ordered multivariate failure time or recurrent event duration data are commonly observed in biomedical longitudinal studies. In general, standard hazard regression methods cannot be applied because of correlation between recurrent failure times within a subject and induced dependent censoring. Multiplicative and additive hazards models provide the two principal frameworks for studying the association between risk factors and recurrent event durations for the analysis of multivariate failure time data.
Using emergency department visits data, we illustrated and compared the additive and multiplicative hazards models for analysis of recurrent event durations under (i) a varying baseline with a common coefficient effect and (ii) a varying baseline with an order-specific coefficient effect.
The analysis showed that both additive and multiplicative hazards models, with varying baseline and common coefficient effects, gave similar results with regard to covariates selected to remain in the model of our real dataset. The confidence intervals of the multiplicative hazards model were wider than the additive hazards model for each of the recurrent events. In addition, in both models, the confidence interval gets wider as the revisit order increased because the risk set decreased as the order of visit increased.
Due to the frequency of multiple failure times or recurrent event duration data in clinical and epidemiologic studies, the multiplicative and additive hazards models are widely applicable and present different information. Hence, it seems desirable to use them, not as alternatives to each other, but together as complementary methods, to provide a more comprehensive understanding of data.
顺序有序的多变量失效时间或复发性事件持续时间数据在生物医学纵向研究中经常观察到。通常，由于个体内复发性失效时间之间的相关性和诱导的依赖删失，标准风险回归方法无法应用。乘法和加法风险模型为研究风险因素与复发性事件持续时间之间的关联提供了两种主要框架，用于分析多变量失效时间数据。
使用急诊科就诊数据，我们说明了和比较了加法和乘法风险模型，用于分析（i）具有共同系数效应的变化基线和（ii）具有顺序特定系数效应的变化基线的复发性事件持续时间。
分析表明，对于选择保留在我们真实数据集模型中的协变量，加法和乘法风险模型（具有变化的基线和共同系数效应）都给出了相似的结果。对于每个复发性事件，乘法风险模型的置信区间都比加法风险模型宽。此外，在这两个模型中，随着随访顺序的增加，置信区间变宽，因为随着访问顺序的增加，风险集减少。
由于临床和流行病学研究中多次失效时间或复发性事件持续时间数据的频率较高，乘法和加法风险模型具有广泛的适用性，并提供了不同的信息。因此，似乎需要使用它们，而不是相互替代，而是作为互补方法一起使用，以更全面地理解数据。
