A Comparison of Methods for Estimating the Causal Effect of a Treatment in Randomized Clinical Trials Subject to Noncompliance
- 28 May 2009
- journal article
- research article
- Published by Oxford University Press (OUP) in Biometrics
- Vol. 65 (2) , 640-649
- https://doi.org/10.1111/j.1541-0420.2008.01066.x
Abstract
We consider the analysis of clinical trials that involve randomization to an active treatment (T = 1) or a control treatment (T = 0), when the active treatment is subject to all-or-nothing compliance. We compare three approaches to estimating treatment efficacy in this situation: as-treated analysis, per-protocol analysis, and instrumental variable (IV) estimation, where the treatment effect is estimated using the randomization indicator as an IV. Both model- and method-of-moment based IV estimators are considered. The assumptions underlying these estimators are assessed, standard errors and mean squared errors of the estimates are compared, and design implications of the three methods are examined. Extensions of the methods to include observed covariates are then discussed, emphasizing the role of compliance propensity methods and the contrasting role of covariates in these extensions. Methods are illustrated on data from the Women Take Pride study, an assessment of behavioral treatments for women with heart disease.Keywords
This publication has 28 references indexed in Scilit:
- On estimating treatment effects under non‐compliance in randomized clinical trials: are intent‐to‐treat or instrumental variables analyses perfect solutions?Statistics in Medicine, 2006
- Bootstrap Tests for Distributional Treatment Effects in Instrumental Variable ModelsJournal of the American Statistical Association, 2002
- Analyzing a Randomized Cancer Prevention Trial with a Missing Binary Outcome, an Auxiliary Variable, and All-or-None ComplianceJournal of the American Statistical Association, 2000
- Analyzing a Randomized Cancer Prevention Trial with a Missing Binary Outcome, an Auxiliary Variable, and All-or-None ComplianceJournal of the American Statistical Association, 2000
- Causal Inference in a Placebo-Controlled Clinical Trial With Binary Outcome and Ordered ComplianceJournal of the American Statistical Association, 1996
- Causal Inference in a Placebo-Controlled Clinical Trial with Binary Outcome and Ordered ComplianceJournal of the American Statistical Association, 1996
- Identification of Causal Effects Using Instrumental Variables: CommentJournal of the American Statistical Association, 1996
- Identification of Causal Effects Using Instrumental VariablesJournal of the American Statistical Association, 1996
- The central role of the propensity score in observational studies for causal effectsBiometrika, 1983
- Assignment to Treatment Group on the Basis of a CovariateJournal of Educational Statistics, 1977