A Nested Dirichlet Process Analysis of Cluster Randomized Trial Data With Application in Geriatric Care Assessment

In cluster randomized trials, patients seen by the same physician are randomized to the same treatment arm as a group. Besides the natural clustering of patients due to cluster/group randomization, interactions between an individual patient and the attending physician within the group could just as well influence patient care outcomes. Despite the intuitive relevance of these interactions to treatment assessment, few studies have thus far examined their influences. Whether and to what extent these interactions affect assessment of the treatment effect remains unexplored. In fact, few statistical models provide ready accommodation for such interactions. In this research, we propose a general modeling framework based on the nested Dirichlet process (nDP) for assessing treatment effect in cluster randomized trials. The proposed methodology explicitly accounts for physician–patient interactions by assuming that the interactions follow unspecified group-specific distributions from an nDP. In addition to accoun...