Relaxing Latent Ignorability in the ITT Analysis of Randomized Studies with Missing Data and Noncompliance
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Abstract:
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.
Subject Area:
Clinical Trials
Suggested Citation:
L Taylor and Xiao-Hua Zhou, "Relaxing Latent Ignorability in the ITT Analysis of Randomized Studies with Missing Data and Noncompliance" (February 19, 2009). UW Biostatistics Working Paper Series. Working Paper 257.
http://www.bepress.com/uwbiostat/paper257