School of Economics and Statistics, Guangzhou University, Guangzhou, 510006, China.
Institute of Applied Mathematics, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, China.
Lifetime Data Anal. 2023 Jul;29(3):672-697. doi: 10.1007/s10985-023-09596-6. Epub 2023 Mar 23.
Interval-censored failure time data arise commonly in various scientific studies where the failure time of interest is only known to lie in a certain time interval rather than observed exactly. In addition, left truncation on the failure event may occur and can greatly complicate the statistical analysis. In this paper, we investigate regression analysis of left-truncated and interval-censored data with the commonly used additive hazards model. Specifically, we propose a conditional estimating equation approach for the estimation, and further improve its estimation efficiency by combining the conditional estimating equation and the pairwise pseudo-score-based estimating equation that can eliminate the nuisance functions from the marginal likelihood of the truncation times. Asymptotic properties of the proposed estimators are discussed including the consistency and asymptotic normality. Extensive simulation studies are conducted to evaluate the empirical performance of the proposed methods, and suggest that the combined estimating equation approach is obviously more efficient than the conditional estimating equation approach. We then apply the proposed methods to a set of real data for illustration.
区间删失失效时间数据在各种科学研究中经常出现，其中感兴趣的失效时间仅知位于某个时间区间内，而不是精确观察到。此外，失效事件可能存在左截断，这会极大地复杂化统计分析。在本文中，我们研究了常用的加性风险模型在左截断和区间删失数据中的回归分析。具体来说，我们提出了一种条件估计方程方法进行估计，并通过将条件估计方程与基于成对拟似然的估计方程相结合，进一步提高了估计效率，这种方法可以从截断时间的边缘似然中消除干扰函数。讨论了所提出估计量的渐近性质，包括一致性和渐近正态性。通过广泛的模拟研究评估了所提出方法的经验性能，并表明组合估计方程方法明显比条件估计方程方法更有效。然后，我们将所提出的方法应用于一组实际数据进行说明。
