School of Mathematics, Jilin University, Changchun, PR China.
Department of Mathematics and Statistics, University of Regina, Regina, Canada.
Stat Methods Med Res. 2020 Aug;29(8):2041-2062. doi: 10.1177/0962280219882968. Epub 2019 Oct 23.
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.
估计从疾病诊断到终末事件的医疗成本是研究人员非常感兴趣的。然而，大多数关于此类研究的现有文献都集中在历史过程的累积均值函数（CMF）的估计上。在本文中，采用逆概率删失加权（IPCW）技术和纵向分位数回归模型的组合方案，提出了一种基于具有时变协变量和右删失时间事件变量的历史过程的累积分位数函数（CQF）估计的新方法。推导了所提出估计器的一致性。进行了广泛的模拟研究，以研究本文中给出的估计器的性能。分析了来自多中心自动除颤器植入试验（MADIT）的医疗成本数据，以说明所开发方法的应用。
