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      Principal stratification with predictors of compliance for randomized trials with 2 active treatments.

      Biostatistics (Oxford, England)

      Treatment Outcome, statistics & numerical data, Smoking Cessation, Sensitivity and Specificity, Regression Analysis, methods, Randomized Controlled Trials as Topic, Probability, Patient Compliance, Humans, Follow-Up Studies, Causality, Biometry

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          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. The principal stratification framework permits inference about causal effects among subpopulations characterized by potential compliance. However, in the absence of prior information, there are 2 significant limitations: (1) the causal effects cannot be point identified for some strata and (2) individuals in the subpopulations (strata) cannot be identified. We propose to use additional information-compliance-predictive covariates-to help identify the causal effects and to help describe characteristics of the subpopulations. The probability of membership in each principal stratum is modeled as a function of these covariates. The model is constructed using marginal compliance models (which are identified) and a sensitivity parameter that captures the association between the 2 marginal distributions. We illustrate our methods in both a simulation study and an analysis of data from a smoking cessation trial.

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