Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials
Version of Record online: 4 APR 2008
© 2008, The International Biometric Society
Volume 65, Issue 1, pages 88–95, March 2009
How to Cite
Taylor, L. and Zhou, X. H. (2009), Multiple Imputation Methods for Treatment Noncompliance and Nonresponse in Randomized Clinical Trials. Biometrics, 65: 88–95. doi: 10.1111/j.1541-0420.2008.01023.x
- Issue online: 17 MAR 2009
- Version of Record online: 4 APR 2008
- Received March 2007. Revised January 2008. Accepted January 2008.
- Causal inference;
- Complier average causal effect;
- Missing data;
- Multiple imputation;
- Principal stratification
Summary 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.