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通常出现在各种科学研究中，其中感兴趣的失效时间仅已知位于某个时间间隔内，而不是准确观察到。此外，可能会发生故障事件的左截断，这会使统计分析变得非常复杂。在本文中，我们使用常用的加性风险模型研究了左截断和区间删失数据的回归分析。具体来说，我们提出了一种用于估计的条件估计方程方法，并通过将条件估计方程与可以从截断时间的边际似然中消除滋扰函数的成对伪分数估计方程相结合，进一步提高了其估计效率。讨论了所提出的估计量的渐近性质，包括一致性和渐近正态性。进行了广泛的模拟研究以评估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真实数据以进行说明。