Abstract
Analyses of trials of group administered treatments require an identifier for therapy group to account for clustering by group. All patients randomized to receive the group administered treatment could be assigned an intended group identifier following randomization. Alternatively, an actual group could be based on those patients that comply with group therapy. We investigate the implications for intention-to-treat (ITT) analyses of using either the intended or actual group to adjust for the clustering effect. We also consider causal models using the actual group. A simulation study showed that ITT estimates based on random effects models or GEE with an exchangeable correlation matrix performed much better when using the intended group than the actual group. OLS with robust standard errors performed well with both. Most compliance average causal effect (CACE) models performed well. While practical constraints of the clinical setting may determine the choice between an intended or actual group analyses, it is desirable to record both. An ITT analysis using mixed models can then be fitted using the intended group with data generation assumptions checked by a causal model using the actual group. Where an ITT analysis is based on the actual group, worse outcome for never-takers than compliers may allow one to infer that some estimators are biased toward no treatment effect. The work here is motivated and illustrated by a trial of a group therapy, but also has relevance to trials with treatment related clustering due to therapist examples of which include physical and talking therapies or surgery.