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Activity Number: 504
Type: Topic Contributed
Date/Time: Wednesday, August 5, 2009 : 2:00 PM to 3:50 PM
Sponsor: Biometrics Section
Abstract - #304497
Title: Causal Inference for Randomized Trials of Two Active Treatments Subject to Noncompliance
Author(s): Jason Roy*+ and Yan Ma
Companies: Geisinger Center for Health Research and Weill Medical College of Cornell University
Address: , , 17822,
Keywords: causal inference ; copula ; sensitivity analysis
Abstract:

In behavioral medicine trials, such as smoking cessation trials, 2 or more active treatments are often compared. Noncompliance by some subjects with their assigned treatment poses a challenge to the data analyst. In particular, the joint distribution of the observed and counterfactual compliance variables cannot be identified, without imposing strong assumptions. However, due to randomization, each marginal compliance distribution can be identified. These marginal distributions impose bounds on the joint distribution. Our approach is to use a copula model to link the two marginal distributions, up to a sensitivity parameter. We then take a principal stratification approach to estimate causal effects. We develop this approach when compliance is either binary (yes/no) or continuous (dose).

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