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. 2009 Sep;65(3):675-82.
doi: 10.1111/j.1541-0420.2008.01120.x. Epub 2008 Aug 28.

Identifiability and estimation of causal effects in randomized trials with noncompliance and completely nonignorable missing data

Hua Chen  1 , Zhi Geng, Xiao-Hua Zhou
Affiliations

Identifiability and estimation of causal effects in randomized trials with noncompliance and completely nonignorable missing data

Hua Chen et al. Biometrics. 2009 Sep.

Abstract

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.

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Figures

Figure 1
Figure 1
Real model: CN; supposed model: LI.
Figure 2
Figure 2
Real model: LI; supposed model: CN.
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