Identifiability and Estimation of Causal Effects in Randomized Trials with Noncompliance and Completely Nonignorable Missing Data

作者：Chen Hua^*; Geng Zhi; Zhou Xiao Hua

来源：Biometrics, 2009, 65(3): 675-682.

DOI：10.1111/j.1541-0420.2008.01120.x

• Causal inference
• Identifiability
• Maximum likelihood estimates
• Missing data
• Noncompliance
• Nonignorable

P>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.

• 出版日期2009-9
• 单位北京大学