Penalized survival models for the analysis of alternating recurrent event data |
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Authors: | Lili Wang Kevin He Douglas E. Schaubel |
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Affiliation: | 1. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan;2. Department of Biostatistics, Epidemiology, and Informatics, University of Pennslyvania, Philadelphia, Pennslyvania |
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Abstract: | Recurrent event data are widely encountered in clinical and observational studies. Most methods for recurrent events treat the outcome as a point process and, as such, neglect any associated event duration. This generally leads to a less informative and potentially biased analysis. We propose a joint model for the recurrent event rate (of incidence) and duration. The two processes are linked through a bivariate normal frailty. For example, when the event is hospitalization, we can treat the time to admission and length-of-stay as two alternating recurrent events. In our method, the regression parameters are estimated through a penalized partial likelihood, and the variance-covariance matrix of the frailty is estimated through a recursive estimating formula. Moreover, we develop a likelihood ratio test to assess the dependence between the incidence and duration processes. Simulation results demonstrate that our method provides accurate parameter estimation, with a relatively fast computation time. We illustrate the methods through an analysis of hospitalizations among end-stage renal disease patients. |
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Keywords: | alternating recurrent events correlated frailty model end-stage renal disease penalized partial likelihood |
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