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Keywords:

  • All-or-none compliance;
  • Causal effect;
  • Multinomial outcomes;
  • Noncompliance;
  • Perfect fit;
  • Principal stratification;
  • Randomized trials

Summary Recently, Cheng (2009, Biometrics65, 96–103) proposed a model for the causal effect of receiving treatment when there is all-or-none compliance in one randomization group, with maximum likelihood estimation based on convex programming. We discuss an alternative approach that involves a model for all-or-none compliance in two randomization groups and estimation via a perfect fit or an expectation–maximization algorithm for count data. We believe this approach is easier to implement, which would facilitate the reproduction of calculations.