Liu Dandan, Kalbfleisch John D, Schaubel Douglas E
Department of Biostatistics, Vanderbilt University School of Medicine 1161 21st Avenue South, Nashville, TN 37232, USA
Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, 48109-2029 U.S.A.
Stat Biosci. 2014 May 1;6(1):19-37. doi: 10.1007/s12561-012-9075-4.
In this article, we develop methods for quantifying center effects with respect to recurrent event data. In the models of interest, center effects are assumed to act multiplicatively on the recurrent event rate function. When the number of centers is large, traditional estimation methods that treat centers as categorical variables have many parameters and are sometimes not feasible to implement, especially with large numbers of distinct recurrent event times. We propose a new estimation method for center effects which avoids including indicator variables for centers. We then show that center effects can be consistently estimated by the center-specific ratio of observed to expected cumulative numbers of events. We also consider the case where the recurrent event sequence can be stopped permanently by a terminating event. Large sample results are developed for the proposed estimators. We assess the finite-sample properties of the proposed estimators through simulation studies. The method is then applied to national hospital admissions data for end stage renal disease patients.
在本文中，我们开发了针对复发事件数据量化中心效应的方法。在感兴趣的模型中，假定中心效应以乘法方式作用于复发事件率函数。当中心数量众多时，将中心视为分类变量的传统估计方法会有许多参数，并且有时难以实施，尤其是在有大量不同复发事件时间的情况下。我们提出了一种用于中心效应的新估计方法，该方法避免纳入中心的指示变量。然后我们表明，中心效应可以通过事件的观察累积数与期望累积数的中心特定比率来一致地估计。我们还考虑了复发事件序列可因终止事件而永久停止的情况。针对所提出的估计量推导了大样本结果。我们通过模拟研究评估所提出估计量的有限样本性质。然后将该方法应用于终末期肾病患者的全国医院入院数据。
