Department of Preventive Medicine (Biostatistics), Feinberg School of Medicine, Northwestern University, Chicago, IL, USA.
Department of Biostatistics and Data Science, University of Texas Health Science Center at Houston, Houston, TX, USA.
Stat Methods Med Res. 2021 May;30(5):1332-1346. doi: 10.1177/0962280221995977. Epub 2021 Mar 20.
The inactivity time, or lost lifespan specifically for mortality data, concerns time from occurrence of an event of interest to the current time point and has recently emerged as a new summary measure for cumulative information inherent in time-to-event data. This summary measure provides several benefits over the traditional methods, including more straightforward interpretation yet less sensitivity to heavy censoring. However, there exists no systematic modeling approach to inferring the quantile inactivity time in the literature. In this paper, we propose a semi-parametric regression method for the quantiles of the inactivity time distribution under right censoring. The consistency and asymptotic normality of the regression parameters are established. To avoid estimation of the probability density function of the inactivity time distribution under censoring, we propose a computationally efficient method for estimating the variance-covariance matrix of the regression coefficient estimates. Simulation results are presented to validate the finite sample properties of the proposed estimators and test statistics. The proposed method is illustrated with a real dataset from a clinical trial on breast cancer.
静止时间，或特定于死亡率数据的丧失寿命，是指从感兴趣事件发生到当前时间点的时间，最近已成为时间事件数据中固有累积信息的新综合指标。与传统方法相比，该综合指标具有几个优势，包括更直接的解释，而对重度删失的敏感性较低。然而，在文献中，没有系统的建模方法来推断静止时间的分位数。在本文中，我们提出了一种在右删失下对静止时间分布分位数进行半参数回归的方法。建立了回归参数的一致性和渐近正态性。为了避免在删失下估计静止时间分布的概率密度函数，我们提出了一种计算效率高的方法来估计回归系数估计值的方差-协方差矩阵。给出了模拟结果以验证所提出估计量和检验统计量的有限样本性质。该方法通过乳腺癌临床试验的真实数据集进行了说明。
