Quantile regression on inactivity time

The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event interest to the current point and has recently emerged as a new summary measure cumulative information inherent in time-to-event data. This provides several benefits over traditional methods, including more straightforward interpretation yet less sensitivity heavy censoring. However, there exists no systematic modeling approach inferring quantile literature. In this paper, we propose semi-parametric regression method quantiles distribution under right consistency asymptotic normality parameters are established. To avoid estimation probability density function censoring, computationally efficient estimating variance–covariance matrix coefficient estimates. Simulation results presented validate finite sample properties proposed estimators test statistics. is illustrated with real dataset clinical trial on breast cancer.