Metcalfe Chris, Thompson Simon G
Department of Social Medicine, University of Bristol, Canynge Hall, Whiteladies Road, Bristol BS8 2PR, UK.
Stat Med. 2006 Jan 15;25(1):165-79. doi: 10.1002/sim.2310.
Statistical methods for the analysis of recurrent events are often evaluated in simulation studies. A factor rarely varied in such studies is the underlying event generation process. If the relative performance of statistical methods differs across generation processes, then studies based upon one process may mislead. This paper describes the simulation of recurrent events data using four models of the generation process: Poisson, mixed Poisson, autoregressive, and Weibull. For each model four commonly used statistical methods for the analysis of recurrent events (Cox's proportional hazards method, the Andersen-Gill method, negative binomial regression, the Prentice-Williams-Peterson method) were applied to 200 simulated data sets, and the mean estimates, standard errors, and confidence intervals obtained. All methods performed well for the Poisson process. Otherwise, negative binomial regression only performed well for the mixed Poisson process, as did the Andersen-Gill method with a robust estimate of the standard error. The Prentice-Williams-Peterson method performed well only for the autoregressive and Weibull processes. So the relative performance of statistical methods depended upon the model of event generation used to simulate data. In conclusion, it is important that simulation studies of statistical methods for recurrent events include simulated data sets based upon a range of models for event generation.
用于分析复发事件的统计方法通常在模拟研究中进行评估。在这类研究中很少变化的一个因素是潜在的事件生成过程。如果统计方法的相对性能在不同的生成过程中有所不同，那么基于一个过程的研究可能会产生误导。本文描述了使用四种事件生成过程模型对复发事件数据进行模拟：泊松模型、混合泊松模型、自回归模型和威布尔模型。对于每个模型，将四种常用的复发事件分析统计方法（考克斯比例风险法、安德森 - 吉尔法、负二项回归、普伦蒂斯 - 威廉姆斯 - 彼得森法）应用于200个模拟数据集，并获得均值估计、标准误差和置信区间。所有方法在泊松过程中表现良好。否则，负二项回归仅在混合泊松过程中表现良好，安德森 - 吉尔法在对标准误差进行稳健估计时也是如此。普伦蒂斯 - 威廉姆斯 - 彼得森法仅在自回归和威布尔过程中表现良好。因此，统计方法的相对性能取决于用于模拟数据的事件生成模型。总之，对于复发事件统计方法的模拟研究，纳入基于一系列事件生成模型的模拟数据集非常重要。
