Semiparametric Model for Recurrent Event Data Under Two Independent Competing Risks with Excess Zero and Informative Censoring

Recurrent event data are often encountered in longitudinal studies and in many other important areas such as Biomedical science, Econometrics, Reliability, Criminology and so on. Several marginal rates models have been used extensively to analyze recurrent event data, but often fail to fit the data adequately. In addition, the analysis is complicated by excess zeros in data as well as the presence of a terminal event. Furthermore effect of competing risks in recurrent event data analyze is rarely investigated. A semiparametric model with additive rate function is introduced in this paper in which unspecified baseline is applied to analyze recurrent event data with zero-recurrence and terminal event. Moreover multiple specified hazard function is used for analysis of competing risks, which includes a parameter to accommodate excess zeros and a frailty term to take into account for a terminal event. Taylor expansion is used to decompose baseline hazard function for each competing risk separately, and local likelihood procedure is applied to estimate parameters. Finally, a simulation study is presented for two specified baseline hazard functions with two different competing risks which approve the performance of presented model against existing models. An illustrative application based on a real bone marrow transplantation data is prepared.