Estimating the quantile medical cost under time-dependent covariates and right censored time-to-event variable based on a state process.
Estimating the medical costs from disease diagnosis to a terminal event is of immense interest to researchers. However, most of existing literature on such research focused on the estimation of cumulative mean function (CMF) for history process. In this paper, the combined scheme of both inverse probability of censoring weighting (IPCW) technique and longitudinal quantile regression model is used to develop a novel procedure to the estimation of cumulative quantile function (CQF) based on history process with time-dependent covariates and right censored time-to-event variable. The consistency of proposed estimator is derived. The extensive simulation study is conducted to investigate the performance of the estimator given in this paper. A medical cost data from a multicenter automatic defibrillator implantation trial (MADIT) is analyzed to illustrate the application of developed method.
['Liu|Xiufang|X|', 'Deng|Dianliang|D|0000-0002-4128-7920', 'Wang|Dehui|D|']
D003198:Computer Simulation D015233:Models, Statistical D011336:Probability D012107:Research Design
history process inverse probability of censoring weighting longitudinal quantile regression Cumulative quantile function medical cost data