NDLI: Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials

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Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials

Content Provider	Scilit
Author	Taylor, L. Zhou, X. H.
Copyright Year	2008
Description	Journal: Biometrics Randomized clinical trials are a powerful tool for investigating causal treatment effects, but in human trials there are oftentimes problems of noncompliance which standard analyses, such as the intention-to-treat or as-treated analysis, either ignore or incorporate in such a way that the resulting estimand is no longer a causal effect. One alternative to these analyses is the complier average causal effect (CACE) which estimates the average causal treatment effect among a subpopulation that would comply under any treatment assigned. We focus on the setting of a randomized clinical trial with crossover treatment noncompliance (e.g., control subjects could receive the intervention and intervention subjects could receive the control) and outcome nonresponse. In this article, we develop estimators for the CACE using multiple imputation methods, which have been successfully applied to a wide variety of missing data problems, but have not yet been applied to the potential outcomes setting of causal inference. 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.
Related Links	http://biostats.bepress.com/cgi/viewcontent.cgi?article=1141&context=uwbiostat http://onlinelibrary.wiley.com/doi/10.1111/j.1541-0420.2008.01023.x/pdf
Ending Page	95
Page Count	8
Starting Page	88
e-ISSN	15410420
DOI	10.1111/j.1541-0420.2008.01023.x
Journal	Biometrics
Issue Number	1
Volume Number	65
Language	English
Publisher	Wiley-Blackwell
Publisher Date	2008-04-04
Access Restriction	Open
Subject Keyword	Journal: Biometrics Statistics and Probability Complier Average Causal Effect Multiple Imputation Principal Stratification
Content Type	Text
Resource Type	Article

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