Premium
A Comparison of Methods for Estimating the Causal Effect of a Treatment in Randomized Clinical Trials Subject to Noncompliance
Author(s) -
Little Roderick J.,
Long Qi,
Lin Xihong
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01066.x
Subject(s) - covariate , estimator , causal inference , randomization , instrumental variable , statistics , clinical trial , randomized controlled trial , econometrics , propensity score matching , medicine , computer science , mathematics , surgery , pathology
Summary 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.