019 - Bayesian Binary Network Autocorrelation Model and Its Application to the Study of Institutional Peer-Effects Involving Adoption of Robotic Surgery

Despite the extensive use of network autocorrelation models in social network analysis, network autocorrelation models for binary dependent variables have received surprisingly scant attention. In this paper, we develop a binary network autocorrelation model using a Bayesian approach for model estimation. We develop multiple prior distributions for the focal peer effect parameter (ρ) designed to improve the performance of these estimators while introducing minimal information into the analysis. These priors include uniform priors with different ranges, Jeffreys priors, and a newly-developed uniform prior on a transformation of ρ. The performance of the proposed Bayesian approach and the sensitivity of results to the prior are assessed using a simulation study. Finally, we construct a New England region patient-sharing hospital network and apply our approaches to study the adoption of robotic surgery among hospitals using a cohort of Medicare beneficiaries in 2016 and 2017.

Binary network autocorrelation model

Peer effect

Bayesian inference

Jeffreys prior

Robotic surgery 

Mid-Level

Influence

International Conference on Health Policy Statistics 2023