"Joint Modeling Compliance and Outcome for Causal Analysis in Longitudinal Studies"
March 1, 2011 @ 3:30 p.m. - 4:30 p.m.
Xin Gao, University of Michigan
Location: 701 Blockley Hall
Joint Modeling Compliance and Outcome
for Causal Analysis in
Randomized studies are well accepted as a standard method to estimate the effect of treatment because they remove both observed and unobserved confounding. However, since patients can choose whether or not to comply with their assigned treatment in many circumstances, non-compliance behavior is common in the randomized studies. In the recent years, a variety of methods have been developed to provide valid estimate of causal effects of treatment for randomized studies when non-compliance is present.
Motivated by this, we proposed a Markov compliance and outcome model to jointly model longitudinal measurements of compliance and outcome for randomized studies in presence of non-compliance. In the proposed causal model, we used the potential outcome framework to define pre-randomization principal strata determined by the joint distribution of compliance under treatment and control arms, and estimate the effect of treatment within each principal stratum. A unique advance of our model is to estimate the impact of the causal effect of treatment at the end of follow up interval t on the compliance in the follow up interval t+1. We applied the proposed causal model in a longitudinal mental health study with psychiatric data, and utilized Bayesian methods with Markov chain Monte Carlo algorithm and data augmentation method for data analysis.