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Bayesian exponential random graph modelling of interhospital patient referral networks
Author(s) -
Caimo Alberto,
Pallotti Francesca,
Lomi Alessandro
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7301
Subject(s) - exponential random graph models , computer science , referral , probabilistic logic , computation , approximate bayesian computation , bayesian probability , exponential function , graph , bayesian network , random graph , theoretical computer science , econometrics , operations research , artificial intelligence , algorithm , medicine , mathematics , family medicine , mathematical analysis , inference
Using original data that we have collected on referral relations between 110 hospitals serving a large regional community, we show how recently derived Bayesian exponential random graph models may be adopted to illuminate core empirical issues in research on relational coordination among healthcare organisations. We show how a rigorous Bayesian computation approach supports a fully probabilistic analytical framework that alleviates well‐known problems in the estimation of model parameters of exponential random graph models. We also show how the main structural features of interhospital patient referral networks that prior studies have described can be reproduced with accuracy by specifying the system of local dependencies that produce – but at the same time are induced by – decentralised collaborative arrangements between hospitals. Copyright © 2017 John Wiley & Sons, Ltd.