The accelerated failure time model is widely used for analyzing censored survival times often observed in clinical studies. It is well-known that the ordinary maximum likelihood estimators of the parameters in the accelerated failure time model are generally sensitive to potential outliers or small deviations from the underlying distributional assumptions. In this paper, we propose and explore a robust method for fitting the accelerated failure time model to survival data by bounding the influence of outliers in both the outcome variable and associated covariates. We also develop a sandwich-type variance–covariance function for approximating the variances of the proposed robust estimators. The finite-sample properties of the estimators are investigated based on empirical results from an extensive simulation study. An application is provided using actual data from a clinical study of primary breast cancer patients.

中文翻译：

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加速故障时间模型中的稳健估计。

加速失效时间模型被广泛用于分析在临床研究中经常观察到的删失生存时间。众所周知，加速故障时间模型中参数的普通最大似然估计量通常对潜在异常值或与基础分布假设的微小偏差敏感。在本文中，我们通过限制异常变量在结果变量和相关协变量中的影响，提出并探索了一种将加速故障时间模型拟合到生存数据的鲁棒方法。我们还开发了一种三明治型方差-协方差函数，用于近似近似所提出的鲁棒估计量的方差。基于大量模拟研究的经验结果，对估计量的有限样本性质进行了研究。