Multivariate Recurrent Events in the Presence of Multivariate Informative Censoring with Applications to Bleeding and Transfusion Events in Myelodysplastic Syndrome

Abstract

We propose a general novel class of joint models to analyze recurrent events that has a wide variety of applications. The focus in this article is to model the bleeding and transfusion events in myelodysplastic syndrome (MDS) studies, where patients may die or withdraw from the study early due to adverse events or other reasons, such as consent withdrawal or required alternative therapy during the study. The proposed model accommodates multiple recurrent events and multivariate informative censoring through a shared random-effects model. The random-effects model captures both within-subject and within-event dependence simultaneously. We construct the likelihood function for the semiparametric joint model and develop an expectation–maximization (EM) algorithm for inference. The computational burden does not increase with the number of types of recurrent events. We utilize the MDS clinical trial data to illustrate our proposed methodology. We also conduct a number of simulations to examine the performance of the proposed model.

Key Words:

• Bleeding events
• EM algorithm
• Semiparametric joint model
• Simulation
• Transfusions

ACKNOWLEDGMENTS

We thank the editor, an associate editor, and two referees for their helpful comments and suggestions, which have led to improvement of this article. We also thank Xin Zhou for helping to program the proposed algorithm in SAS.

Notes

Color versions of one or more of the figures in the article can be found online at www.tandfonline.com/lbps.