Abstract

In lifetime data analysis it is important to know the effect of covariates on the lifetime of a subject. The proportional hazards and odds models are well discussed in the right censored data to incorporate the covariate effect on Survival times. Apart from right censoring, interval censoring is common in survival studies. Competing risks emerge naturally in lifetime data analysis when the subjects under study are at risk for more than one cause of failure. Motivated by these scenarios, we develop a linear transformation model for competing risks under length-biased interval censored data. We develop counting process based estimating equation for finding the estimates of regression parameters. Asymptotic properties of the regression estimates are studied. A Monte Carlo simulation study is conducted to evaluate the finite sample performance of the proposed estimators. Finally, We illustrate, the methodology using a real data.