Fitting a Quantile Regression Model for Residual Life with the R Package qris

In survival analysis, regression modeling has traditionally focused on assessing covariate effects on survival times, which is defined as the elapsed time between a baseline and event time. Nevertheless, focusing on residual life can provide a more dynamic assessment of covariate effects, as it offers more updated information at specific time points between the baseline and event occurrence. Statistical methods for fitting quantile regression models have recently been proposed, providing favorable alternatives to modeling the mean of residual lifetimes. Despite these progresses, the lack of computer software that implements these methods remains an obstacle for researchers analyzing data in practice. In this paper, we introduce an R package qris, which implements methods for fitting semiparametric quantile regression models on residual life subject to right censoring. We demonstrate the effectiveness and versatility of this package through comprehensive simulation studies and a real-world data example, showcasing its valuable contributions to survival analysis research.