Causal inference for randomized trials with noncompliance

Noncompliance is a common problem in randomized trials. When there is noncompliance, there is often interest in estimating the causal effect of actually receiving the treatment compared to receiving the control. Standard intent-to-treat analyses that compare randomization arms without regard to compliance do not estimate the causal effect of receiving treatment. My dissertation studies methods for making more accurate inferences about the causal effects of receiving treatment in randomized clinical trials when there is noncompliance. The first part of my dissertation focuses on causal methods for three-arm trials. We develop a method for constructing sharp bounds and confidence intervals for the average effects of receiving treatment within principal strata in three-arm trials with noncompliance. The first chapter in the second part of my dissertation develops a new efficient nonparametric estimator for the Complier Average Causal Effect (CACE) in two-arm trials with noncompliance. By using the empirical likelihood approach to construct a profile random sieve likelihood and taking into account the mixture structure in outcome distributions, our estimator is robust to parametric distribution assumptions and more efficient than the standard instrumental variables estimator. The second chapter in the second part of my dissertation develops a bootstrap version of a likelihood ratio test for the CACE in two-arm trials with noncompliance which have a multinomial outcome. We apply our method in the first part to the data from a randomized trial of treatments for alcohol dependence, and methods in the second part to the data from a randomized trial of an encouragement intervention to improve adherence to prescribed depression treatments among depressed elderly patients in primary care practices