A new model for joint analysis of recurrent and terminal events is proposed. Conditional on a random effect, the intensity for the recurrent event process has a multiplicative form and a Cox model is formulated for the terminal event process. The two processes are linked via a copula function which defines a joint model for the random effect and the terminal event. When these processes are subject to right-censoring, the observed data likelihood can be maximized directly or via an EM algorithm; the latter facilitates joint semiparametric analysis. Variance estimates are derived based on Louis’ method. A computationally more convenient two-stage estimation procedure is also investigated. Simulation studies demonstrate good finite sample performance under simultaneous and two-stage estimation, and an application to a study of effect of pamidronate on reducing skeletal complications in patient with skeletal metastases illustrates the use of this model.
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