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Relational event models for longitudinal network data with an application to interhospital patient transfers

Relational event models for longitudinal network data with an application to interhospital patient transfers

Vu, Duy, Lomi, Alessandro, Mascia, Daniele and Pallotti, Francesca (2017) Relational event models for longitudinal network data with an application to interhospital patient transfers. Statistics in Medicine, 36 (14). pp. 2265-2287. ISSN 0277-6715 (Print), 1097-0258 (Online) (doi:https://doi.org/10.1002/sim.7247)

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Abstract

The main objective of this paper is to introduce and illustrate relational event models, a new class of statistical models for the analysis of time-stamped data with complex temporal and relational dependencies. We outline the main differences between recently proposed relational event models and more conventional network models based on the graph-theoretic formalism typically adopted in empirical studies of social networks. Our main contribution involves the definition and implementation of a marked point process extension of currently available models. According to this approach, the sequence of events of interest is decomposed into two components: (a) event time, and (b) event destination. This decomposition transforms the problem of selection of event destination in relational event models into a conditional multinomial logistic regression problem. The main advantages of this formulation are the possibility of controlling for the effect of event-specific data and a significant reduction in the estimation time of currently available relational event models. We demonstrate the empirical value of the model in an analysis of interhospital patient transfer within a regional community of health care organizations. We conclude with a discussion of how the models we presented help to overcome some the limitations of statistical models for networks that are currently available.

Item Type: Article
Uncontrolled Keywords: Social network analysis; Relational event models; Inter-organizational relations, Interhospital patient transfers
Subjects: H Social Sciences > HA Statistics
Faculty / Department / Research Group: Faculty of Business
Faculty of Business > Centre for Business Network Analysis (CBNA)
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: 31 Mar 2018 00:38
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/16323

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