On the Plackett Distribution with Bivariate Censored Data

Abstract

In the analysis of dependence of bivariate correlated failure time data, a popular model is a gamma frailty model proposed by Clayton and Oakes. An alternative approach is using a Plackett distribution, whose dependence parameter has a very appealing odds ratio interpretation for dependence between the two failure times. In this article, we develop novel semiparametric estimation and inference procedures for the model. The asymptotic results of the estimator are developed; in addition, a goodness of fit test is also developed. We also discuss a regression extension to adjust for covariates using the linear regression model as well as applications to semi-competing risks data. The performance of the proposed techniques in finite samples is examined using simulation studies. Several real-data examples are used to illustrate the methodology.