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Statistica Sinica 19 (2009), 749-764





RELAXING LATENT IGNORABILITY IN THE ITT

ANALYSIS OF RANDOMIZED STUDIES

WITH MISSING DATA AND NONCOMPLIANCE


Leslie Taylor$^1$ and Xiao-Hua Zhou$^{1,2}$


$^1$University of Washington and $^2$Northwest HSR&D Center of Excellence


Abstract: In this paper we consider the problem in causal inference of estimating the local complier average causal effect (CACE) parameter in the setting of a randomized clinical trial with a binary outcome, cross-over noncompliance, and unintentional missing data on the responses. We focus on the development of a moment estimator that relaxes the assumption of latent ignorability and incorporates sensitivity parameters that represent the relationship between potential outcomes and associated potential response indicators. If conclusions are insensitive over a range of logically possible values of the sensitivity parameters, then the number of interpretations of the data is reduced, and causal conclusions are more defensible. We illustrate our methods using a randomized encouragement design study on the effectiveness of an influenza vaccine.



Key words and phrases: Causal inference, complier average causal effect, encouragement design study, flu shots, latent ignorability, missing data, noncompliance.

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