Zhao Xingqiu, Liu Li, Liu Yanyan, Xu Wei
Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong.
Biom J. 2012 Sep;54(5):585-99. doi: 10.1002/bimj.201100194. Epub 2012 Aug 7.
Multivariate recurrent event data are usually encountered in many clinical and longitudinal studies in which each study subject may experience multiple recurrent events. For the analysis of such data, most existing approaches have been proposed under the assumption that the censoring times are noninformative, which may not be true especially when the observation of recurrent events is terminated by a failure event. In this article, we consider regression analysis of multivariate recurrent event data with both time-dependent and time-independent covariates where the censoring times and the recurrent event process are allowed to be correlated via a frailty. The proposed joint model is flexible where both the distributions of censoring and frailty variables are left unspecified. We propose a pairwise pseudolikelihood approach and an estimating equation-based approach for estimating coefficients of time-dependent and time-independent covariates, respectively. The large sample properties of the proposed estimates are established, while the finite-sample properties are demonstrated by simulation studies. The proposed methods are applied to the analysis of a set of bivariate recurrent event data from a study of platelet transfusion reactions.
多变量复发事件数据通常出现在许多临床和纵向研究中，其中每个研究对象可能经历多次复发事件。对于此类数据的分析，大多数现有方法都是在删失时间无信息的假设下提出的，但这可能并不正确，尤其是当复发事件的观察因失败事件而终止时。在本文中，我们考虑对具有随时间变化和不随时间变化协变量的多变量复发事件数据进行回归分析，其中删失时间和复发事件过程通过脆弱性允许相关。所提出的联合模型很灵活，删失和脆弱变量的分布都未明确指定。我们分别提出了一种成对伪似然方法和一种基于估计方程的方法来估计随时间变化和不随时间变化协变量的系数。建立了所提出估计量的大样本性质，同时通过模拟研究展示了有限样本性质。所提出的方法应用于对一组来自血小板输血反应研究的双变量复发事件数据的分析。
