In this paper we first studied parameter identifiability in randomized clinical trials with noncompliance and missing outcomes. We showed that under certain conditions the parameters of interest were identifiable even under different types of completely non-ignorable missing data, that is, the missing mechanism depends on the outcome.We then derived their maximum likelihood (ML) and moment estimators and evaluated their finite-sample properties in simulation studies in terms of bias, efficiency and robustness. Our sensitive analysis showed the assumed non-ignorable missing- data model had an important impact on the estimated complier average causal effect (CACE) parameter. Our new method provides some new and useful alternative non-ignorable missing-data models over the existing latent ignorable model, which guarantee parameter identifiability, for estimating the CACE in a randomized clinical trial with non-compliance and missing data.



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