Statistical techniques for analyzing data from prevention trials: Treatment of no-shows using Rubin's causal model

Noncompliance is a common challenge in the analysis and interpretation of prevention trials. The authors describe new formulations of the problem based on D. B. Rubin's (1974, 1978) causal model. The formulations help clarify assumptions underlying estimation procedures and yield more efficient methods of estimation. The authors apply the methods to a trial of a job training intervention in which nearly half the participants randomly assigned to the intervention failed to attend the job training seminars. An interesting feature is the presence of covariates measured prior to treatment randomization. Versions of the model that condition on these covariates suggest positive results for the intervention in a high-risk group but no evidence of gains in a low-risk group.