Caimo, Alberto, Pallotti, Francesca and Lomi, Alessandro (2017) Bayesian exponential random graph modelling of interhospital patient referral networks. Statistics in Medicine, 36 (18). pp. 2902-2920. ISSN 0277-6715 (Print), 1097-0258 (Online) (doi:https://doi.org/10.1002/sim.7301)

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Abstract

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 health care 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.

Item Type:	Article
Uncontrolled Keywords:	Bayesian inference; Exponential random graph models; Interorganisational networks interhospital patient referral networks; Monte Carlo methods; statistical models for social networks.
Subjects:	H Social Sciences > HA Statistics
Faculty / School / Research Centre / Research Group:	Faculty of Business Faculty of Business > Networks and Urban Systems Centre (NUSC) > Centre for Business Network Analysis (CBNA) Faculty of Business > Department of International Business & Economics
Last Modified:	19 Apr 2018 00:38
URI:	http://gala.gre.ac.uk/id/eprint/16508

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