Quantile function models for survival data analysis - Yuzhi Cai (Australian and New Zealand Journal of Statistics, 2013)
In this paper we propose a quantile survival model to analyze censored data. Thisapproach provides a very effective way to construct a proper model for the survival timeconditional on some covariates. Once a quantile survival model for the censored data isestablished, the survival density, survival or hazard functions of the survival time can beobtained easily. For illustration purposes, we focus on a model that is based on thegeneralized lambda distribution (GLD). The GLD and many other quantile functionmodels are defined only through their quantile functions, no closed-form expressions areavailable for other equivalent functions. We also develop a Bayesian Markov ChainMonte Carlo (MCMC) method for parameter estimation. Extensive simulation studieshave been conducted. Both simulation study and application results show that theproposed quantile survival models can be very useful in practice.