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Long Q, Little RJ, Lin X. Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework. J R Stat Soc Ser C Appl Stat. 2010;59(3):513-531doi: 10.1111/j.1467-9876.2009.00709.x.
Long, Q., Little, R. J., & Lin, X. (2010). Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework. Journal of the Royal Statistical Society. Series C, Applied statistics, 59(3), 513-531. https://doi.org/10.1111/j.1467-9876.2009.00709.x
Long, Qi, et al. "Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework." Journal of the Royal Statistical Society. Series C, Applied statistics vol. 59,3 (2010): 513-531. doi: https://doi.org/10.1111/j.1467-9876.2009.00709.x
Long Q, Little RJ, Lin X. Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework. J R Stat Soc Ser C Appl Stat. 2010 May;59(3):513-531. doi: 10.1111/j.1467-9876.2009.00709.x. PMID: 21637737; PMCID: PMC3104736.
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J R Stat Soc Ser C Appl Stat. 2010 May;59(3):513-531. doi: 10.1111/j.1467-9876.2009.00709.x.

Estimating Causal Effects in Trials Involving Multi-Treatment Arms Subject to Non-compliance: A Bayesian framework.

Journal of the Royal Statistical Society. Series C, Applied statistics

Qi Long, Roderick J A Little, Xihong Lin

Affiliations

  1. Department of Biostatistics and Bioinformatics, Emory University, Atlanta, GA, 30322, USA.

PMID: 21637737 PMCID: PMC3104736 DOI: 10.1111/j.1467-9876.2009.00709.x

Abstract

Data analysis for randomized trials including multi-treatment arms is often complicated by subjects who do not comply with their treatment assignment. We discuss here methods of estimating treatment efficacy for randomized trials involving multi-treatment arms subject to non-compliance. One treatment effect of interest in the presence of non-compliance is the complier average causal effect (CACE) (Angrist et al. 1996), which is defined as the treatment effect for subjects who would comply regardless of the assigned treatment. Following the idea of principal stratification (Frangakis & Rubin 2002), we define principal compliance (Little et al. 2009) in trials with three treatment arms, extend CACE and define causal estimands of interest in this setting. In addition, we discuss structural assumptions needed for estimation of causal effects and the identifiability problem inherent in this setting from both a Bayesian and a classical statistical perspective. We propose a likelihood-based framework that models potential outcomes in this setting and a Bayes procedure for statistical inference. We compare our method with a method of moments approach proposed by Cheng & Small (2006) using a hypothetical data set, and further illustrate our approach with an application to a behavioral intervention study (Janevic et al. 2003).

References

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  3. Biometrics. 2004 Sep;60(3):598-607 - PubMed
  4. Biostatistics. 2008 Apr;9(2):277-89 - PubMed
  5. Biometrics. 2002 Mar;58(1):21-9 - PubMed

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