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Biostatistics Advance Access originally published online on August 6, 2007
Biostatistics 2008 9(2):277-289; doi:10.1093/biostatistics/kxm027
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© The Author 2007. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oxfordjournals.org.

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

Jason Roy*

Center for Health Research, Geisinger Health System, Danville, PA, USA jaroy{at}geisinger.edu

Joseph W. Hogan

Center for Statistical Sciences, Brown University, Providence, RI, USA

Bess H. Marcus

Centers for Behavioral and Preventive Medicine and Division of Cardiology, Brown Medical School and The Miriam Hospital, Brown University, Providence, RI, USA

* To whom correspondence should be addressed.

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.

Keywords: Bounds; Causal effect; Latent class model; Mediation; Noncompliance; Potential outcomes

Received November 2, 2006; revised June 20, 2007; accepted for publication July 10, 2007.


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