具有连续辅助协变量的均值剩余寿命模型的统计推断

Statistical inference for proportional mean residual life model with continuous auxiliary covariate

在许多实际问题的研究中,由于预算或者技术等因素限制,重要协变量值只能对某一个核实集进行准确测量,而对应重要协变量的连续辅助协变量则对研究队列中的所有个体均可观测.利用辅助协变量的信息有助于提高统计研究的效率.本文考虑如何利用辅助协变量的信息来提高比例均值剩余寿命模型的统计推断效率.本文基于逆概率删失加权技巧构造一个估计方程来估计回归参数,并证明所得估计是相合的和渐近正态的.大量数值模拟验证了所提出的方法在有限样本下的有效性.最后,将所提出的方法应用于Mayo临床试验中原发性胆汁性肝硬化问题的研究中.

In many statistical studies involving failure data, the main covariate is only measured in a validation set due to financial and technical restriction, while some auxiliary information for the main covariate is collected for the full cohort. Making use of the auxiliary information will increase the efficiency of the study. In this paper, we consider statistical inference for the proportional mean residual life model when the primary covariate is measured only for a randomly chosen subcohort and some auxiliary variables are available for the whole study cohort. To further make use of the auxiliary information to improve the efficiency of the study, we propose an estimating equation based on the inverse probability of censoring weighting techniques. The resulting estimators are shown to be consistent and asymptotically normal. Extensive simulations are conducted to evaluate the finite sample performance of the proposed methods. We illustrate the proposed method with a real data set from the Mayo Clinic trial in primary biliary cirrhosis of the liver.