Yu Mengzhu, Feng Yanqin, Duan Ran, Sun Jianguo
Center for Applied Statistical Research and College of Mathematics, 12510Jilin University, China.
School of Mathematics and Statistics, 12390Wuhan University, China.
Stat Methods Med Res. 2022 Mar;31(3):391-403. doi: 10.1177/09622802211061668. Epub 2021 Dec 8.
Regression analysis of multivariate interval-censored failure time data has been discussed by many authors. For most of the existing methods, however, one limitation is that they only apply to the situation where the censoring is non-informative or the failure time of interest is independent of the censoring mechanism. It is apparent that this may not be true sometimes and as pointed out by some authors, the analysis that does not take the dependent censoring into account could lead to biased or misleading results. In this study, we consider regression analysis of multivariate interval-censored data arising from the additive hazards model and propose an estimating equation-based approach that allows for the informative censoring. The method can be easily implemented and the asymptotic properties of the proposed estimator of regression parameters are established. Also we perform a simulation study for the evaluation of the proposed method and it suggests that the method works well for practical situations. Finally, the proposed approach is applied to a set of real data.
许多作者都讨论了多变量区间删失失效时间数据的回归分析。然而，对于大多数现有方法来说，一个局限性在于它们仅适用于删失是非信息性的情况，或者所关注的失效时间与删失机制无关的情况。显然，有时情况并非如此，正如一些作者所指出的，不考虑相依删失的分析可能会导致有偏差或误导性的结果。在本研究中，我们考虑来自加性风险模型的多变量区间删失数据的回归分析，并提出一种基于估计方程的方法，该方法允许存在信息性删失。该方法易于实现，并且建立了所提出的回归参数估计量的渐近性质。我们还进行了一项模拟研究来评估所提出的方法，结果表明该方法在实际情况中效果良好。最后，将所提出的方法应用于一组实际数据。
