Non-parametric estimation of the covariate-dependent bivariate distribution for censored gap times

In many biomedical studies, recurrent or consecutive events may occur during the followup of the individuals. This situation can be found, for example, in transplant studies, where there are two consecutive events which give rise to two times of interest subject to a common random right-censoring time, the frst one being the elapsed time from acceptance into the transplantation program to transplant, and the second one the time from transplant to death. In this work, we incorporate the information of a continuous covariate into the bivariate distribution of the two gap times of interest and propose a non-parametric method to cope with it. We prove the asymptotic properties of the proposed method and carry out a simulation study to see the performance of this approach. Additionally, we illustrate its use with Stanford heart transplant data and colon cancer data.

Non-parametric estimation of the covariate-dependent bivariate distribution for censored gap times Articles