Division of Biostatistics, Department of Public Health Sciences, School of Medicine, University of California, Davis, CA 95616, USA.
Stat Med. 2010 Nov 10;29(25):2592-604. doi: 10.1002/sim.4016.
Several approaches exist for handling missing covariates in the Cox proportional hazards model. The multiple imputation (MI) is relatively easy to implement with various software available and results in consistent estimates if the imputation model is correct. On the other hand, the fully augmented weighted estimators (FAWEs) recover a substantial proportion of the efficiency and have the doubly robust property. In this paper, we compare the FAWEs and the MI through a comprehensive simulation study. For the MI, we consider the multiple imputation by chained equation and focus on two imputation methods: Bayesian linear regression imputation and predictive mean matching. Simulation results show that the imputation methods can be rather sensitive to model misspecification and may have large bias when the censoring time depends on the missing covariates. In contrast, the FAWEs allow the censoring time to depend on the missing covariates and are remarkably robust as long as getting either the conditional expectations or the selection probability correct due to the doubly robust property. The comparison suggests that the FAWEs show the potential for being a competitive and attractive tool for tackling the analysis of survival data with missing covariates.
处理 Cox 比例风险模型中缺失协变量的方法有几种。多重插补（MI）相对容易实施，并且有多种可用的软件，只要插补模型正确，就可以得到一致的估计值。另一方面，完全增广加权估计量（FAWEs）恢复了相当大的效率比例，并且具有双重稳健性。在本文中，我们通过全面的模拟研究比较了 FAWEs 和 MI。对于 MI，我们考虑通过链式方程进行多重插补，并重点关注两种插补方法：贝叶斯线性回归插补和预测均值匹配。模拟结果表明，插补方法对模型的误设定可能非常敏感，并且当删失时间依赖于缺失协变量时，可能会产生很大的偏差。相比之下，FAWEs 允许删失时间依赖于缺失协变量，并且只要由于双重稳健性而正确获得条件期望或选择概率，就具有很强的稳健性。比较表明，FAWEs 有潜力成为处理缺失协变量的生存数据分析的一种有竞争力且有吸引力的工具。
