Investigation of Patient-sharing Networks Using a Bayesian Network Model Selection Approach for Congruence Class Models

Abstract: A Bayesian approach to conduct network model selection is presented for a general class of network models referred to as the congruence class models (CCMs). CCMs form a broad class that includes as special cases several common network models, such as the Erd\H{o}s-R\'{e}nyi-Gilbert model, stochastic block model and many exponential random graph models. Due to the range of models able to be specified as a CCM, investigators are better able to select a model consistent with generative mechanisms associated with the observed network compared to current approaches. In addition, the approach allows for incorporation of prior information. We utilize the proposed Bayesian network model selection approach for CCMs to investigate several mechanisms that may be responsible for the structure of patient-sharing networks, which are associated with the cost and quality of medical care. We found evidence in support of heterogeneity in sociality but not selective mixing by provider type nor degree.