An application of principal stratification to control for institutionalization at follow-up in studies of substance abuse treatment programs

TL;DR

This paper explores using principal stratification to address institutionalization bias in longitudinal substance abuse treatment studies, highlighting its potential and limitations through simulation analysis.

Contribution

It extends principal stratification methodology to model institutionalization effects and demonstrates its application in adolescent substance abuse treatment research.

Findings

Principal stratification can identify well-defined causal effects.

The method requires strict data conditions for accurate recovery of effects.

Caution is needed when applying principal stratification to post-treatment confounders.

Abstract

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.