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

Jason Roy, Joseph W. Hogan, Bess H. Marcus

Research output: Contribution to journalArticlepeer-review

33 Scopus citations

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. 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.

Original languageEnglish (US)
Pages (from-to)277-289
Number of pages13
JournalBiostatistics
Volume9
Issue number2
DOIs
StatePublished - Apr 2008
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Keywords

  • Bounds
  • Causal effect
  • Latent class model
  • Mediation
  • Noncompliance
  • Potential outcomes

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