Noncompliance is a common problem in experiments involving randomized assignment of treatments, and standard analyses based on intention-to treat or treatment received have limitations. An attractive alternative is to estimate the Complier-Average Causal Effect (CACE), which is the average treatment effect for the subpopulation of subjects who would comply under either treatment (Angrist, Imbens and Rubin, 1996, henceforth AIR). We propose an Extended General Location Model to estimate the CACE from data with non-compliance and missing data in the outcome and in baseline covariates. Models for both continuous and categorical outcomes and ignorable and latent ignorable (Frangakis and Rubin, 1999) missing data mechanisms are developed. Inferences for the models are based on the EM algorithm and Bayesian MCMC methods. We present results from simulations that investigate sensitivity to model assumptions and the influence of missing-data mechanism. We also apply the method to the data from a job search intervention for the unemployed workers.
Clinical Trials | Epidemiology | Numerical Analysis and Computation | Statistical Models
Peng, Yahong; Little, Rod; and Raghuanthan, Trivellore E., "An Extended General Location Model for Causal Inference from Data Subject to Noncompliance and Missing Values" (August 2003). The University of Michigan Department of Biostatistics Working Paper Series. Working Paper 7.