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Semiparametric transformation models for joint analysis of multivariate recurrent and terminal events
Liang Zhu1, Jianguo Sun, Deo Kumar Srivastava
1Department of Biostatistics, St. Jude Children's Research Hospital, Memphis, TN 38105, U.S.A.. liang.zhu@stjude.org
Abstract:
Recurrent event data occur in many clinical and observational studies, and in these situations, there may exist a terminal event such as death that is related to the recurrent event of interest. In addition, sometimes more than one type of recurrent events may occur, that is, one may encounter multivariate recurrent event data with some dependent terminal event. For the analysis of such data, one must take into account the dependence among different types of recurrent events and that between the recurrent events and the terminal event. In this paper, we extend a method for univariate recurrent and terminal events and propose a joint modeling approach for regression analysis of the data and establish the finite and asymptotic properties of the resulting estimates of unknown parameters. The method is applied to a set of bivariate recurrent event data arising from a long-term follow-up study of childhood cancer survivors.
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