Abstract
In [13], Schaubel et al. proposed a semiparametric partially linear rate model for the statistical analysis of recurrent event data. But they only considered the model with time-independent covariate effects. In this paper, rate function of the recurrent event is modeled by a semiparametric partially linear function which can include the time-varying effects. We propose the method of generalized estimating equations to make inferences about both the time-varying effects and time-independent effects. The large sample properties are established, while extensive simulation studies are carried out to examine the proposed procedures. At last, we apply the procedures to the well-known bladder cancer study.
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Supported by the National Natural Science Foundation of China (No.11326184) and the Fundamental Research Funds for the Central Universities (No.14CX02146A).
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Chen, Xl. Partially time-varying coefficient linear rate model for the recurrent event data. Acta Math. Appl. Sin. Engl. Ser. 30, 681–698 (2014). https://doi.org/10.1007/s10255-014-0425-5
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DOI: https://doi.org/10.1007/s10255-014-0425-5