Multiple Imputation Methods for Treatment Noncompliance and Missing Data
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Abstract:
Well-designed randomized clinical trials are a powerful tool for investigating causal treatment effects, but in trials involving human subjects there are oftentimes problems of noncompliance which standard analyses, such as the intention-to-treat or as-treated analysis, either ignore or account for in such a way that the estimand can no longer be considered a causal effect. An alternative to these analysis is the complier average causal effect (CACE) which estimates the average causal treatment effect among a subpopulation that would comply under any treatment assigned. In this paper we derive multiple imputation estimators for the CACE using data augmentation algorithms in the setting of a randomized clinical trial with crossover treatment noncompliance and missing data. Using simulated data we investigate the finite sample properties of these estimators as well as of competing procedures in a simple setting. Finally we illustrate our methods using a real randomized encouragement design study on the effectiveness of the influenza vaccine.
Subject Area:
Clinical Trials
Suggested Citation:
Leslie Taylor and Xiao-Hua (Andrew) Zhou, "Multiple Imputation Methods for Treatment Noncompliance and Missing Data" (March 1, 2007). UW Biostatistics Working Paper Series. Working Paper 312.
http://www.bepress.com/uwbiostat/paper312