  Participants in longitudinal studies on the effects of drug treatment and
criminal justice system interventions are at high risk for institutionalization
(e.g., spending time in an environment where their freedom to use drugs, commit
crimes, or engage in risky behavior may be circumscribed). Methods used for
estimating treatment effects in the presence of institutionalization during
follow-up can be highly sensitive to assumptions that are unlikely to be met in
applications and thus likely to yield misleading inferences. In this paper we
consider the use of principal stratification to control for
institutionalization at follow-up. Principal stratification has been suggested
for similar problems where outcomes are unobservable for samples of study
participants because of dropout, death or other forms of censoring. The method
identifies principal strata within which causal effects are well defined and
potentially estimable. We extend the method of principal stratification to
model institutionalization at follow-up and estimate the effect of residential
substance abuse treatment versus outpatient services in a large scale study of
adolescent substance abuse treatment programs. Additionally, we discuss
practical issues in applying the principal stratification model to data. We
show via simulation studies that the model can only recover true effects
provided the data meet strenuous demands and that there must be caution taken
when implementing principal stratification as a technique to control for
post-treatment confounders such as institutionalization.
