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. KGaA, Weinheim.
- Informative censoring
- Marginal model
- Multivariate recurrent event data
- Pairwise pseudolikelihood
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty