School of Mathematics and Statistics, Xi'an Jiaotong University, Xi'an, China.
College of Mathematics and Statistics Institute of Statistical Sciences, Shenzhen Key Laboratory of Advanced Machine Learning and Applications, Shenzhen University, Shenzhen, China.
Lifetime Data Anal. 2021 Oct;27(4):679-709. doi: 10.1007/s10985-021-09526-4. Epub 2021 Jul 2.
In medical studies, the collected covariates contain underlying outliers. For clustered/longitudinal data with censored observations, the traditional Gehan-type estimator is robust to outliers in response but sensitive to outliers in the covariate domain, and it also ignores the within-cluster correlations. To take account of within-cluster correlations, varying cluster sizes, and outliers in covariates, we propose weighted Gehan-type estimating functions for parameter estimation in the accelerated failure time model for clustered data. We provide the asymptotic properties of the resulting estimators and carry out simulation studies to evaluate the performance of the proposed method under a variety of realistic settings. The simulation results demonstrate that the proposed method is robust to the outliers existing in the covariate domain and lead to much more efficient estimators when a strong within-cluster correlation exists. Finally, the proposed method is applied to two medical datasets and more reliable and convincing results are hence obtained.
在医学研究中，收集的协变量包含潜在的异常值。对于具有删失观测值的聚类/纵向数据，传统的 Gehan 型估计量对响应中的异常值具有稳健性，但对协变量域中的异常值敏感，并且它也忽略了聚类内相关性。为了考虑聚类内相关性、变化的聚类大小和协变量中的异常值，我们为聚类数据的加速失效时间模型提出了加权 Gehan 型估计函数，用于参数估计。我们给出了所得估计量的渐近性质，并进行了模拟研究，以在各种实际情况下评估所提出方法的性能。模拟结果表明，当存在强聚类内相关性时，该方法对协变量域中存在的异常值具有稳健性，并导致更有效的估计量。最后，将该方法应用于两个医学数据集，并因此获得了更可靠和令人信服的结果。
