Chen Hua, Geng Zhi, Zhou Xiao-Hua
School of Mathematical Sciences, Peking University, Beijing 100871, China.
Biometrics. 2009 Sep;65(3):675-82. doi: 10.1111/j.1541-0420.2008.01120.x. Epub 2008 Aug 28.
In this article, we first study parameter identifiability in randomized clinical trials with noncompliance and missing outcomes. We show that under certain conditions the parameters of interest are identifiable even under different types of completely nonignorable missing data: that is, the missing mechanism depends on the outcome. We then derive their maximum likelihood and moment estimators and evaluate their finite-sample properties in simulation studies in terms of bias, efficiency, and robustness. Our sensitivity analysis shows that the assumed nonignorable missing-data model has an important impact on the estimated complier average causal effect (CACE) parameter. Our new method provides some new and useful alternative nonignorable missing-data models over the existing latent ignorable model, which guarantees parameter identifiability, for estimating the CACE in a randomized clinical trial with noncompliance and missing data.
在本文中，我们首先研究存在不依从和结局缺失的随机临床试验中的参数可识别性。我们表明，在某些条件下，即使存在不同类型的完全不可忽略的缺失数据（即缺失机制取决于结局），感兴趣的参数仍是可识别的。然后，我们推导了它们的最大似然估计量和矩估计量，并在模拟研究中根据偏差、效率和稳健性评估了它们的有限样本性质。我们的敏感性分析表明，假定的不可忽略缺失数据模型对估计的依从者平均因果效应（CACE）参数有重要影响。我们的新方法在现有的潜在可忽略模型之上提供了一些新的、有用的替代不可忽略缺失数据模型，该模型保证了参数可识别性，用于在存在不依从和缺失数据的随机临床试验中估计CACE。
