Group therapy is a central treatment modality for behavioral health disorders
such as alcohol and other drug use (AOD) and depression. Group therapy is often
delivered under a rolling (or open) admissions policy, where new clients are
continuously enrolled into a group as space permits. Rolling admissions
policies result in a complex correlation structure among client outcomes.
Despite the ubiquity of rolling admissions in practice, little guidance on the
analysis of such data is available. We discuss the limitations of previously
proposed approaches in the context of a study that delivered group cognitive
behavioral therapy for depression to clients in residential substance abuse
treatment. We improve upon previous rolling group analytic approaches by fully
modeling the interrelatedness of client depressive symptom scores using a
hierarchical Bayesian model that assumes a conditionally autoregressive prior
for session-level random effects. We demonstrate improved performance using our
method for estimating the variance of model parameters and the enhanced ability
to learn about the complex correlation structure among participants in rolling
therapy groups. Our approach broadly applies to any group therapy setting where
groups have changing client composition. It will lead to more efficient
analyses of client-level data and improve the group therapy research
community's ability to understand how the dynamics of rolling groups lead to
client outcomes.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS434 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
