Relational event models for longitudinal network data with an application to interhospital patient transfers. (30th March 2017)

• Record Type: Journal Article
• Authors: Vu, Duy; Lomi, Alessandro; Mascia, Daniele; Pallotti, Francesca
• Is Part Of: Statistics in medicine. Volume 36:Number 14(2017)
• Page Start: 2265
• Page End: 2287
• Publication Date: 2017-03-30
• Subjects: social network analysis -- relational event models -- interorganizational relations -- interhospital patient transfers; Medical statistics -- Periodicals; Statistique médicale -- Périodiques; Statistiques médicales -- Périodiques; 610.727
• DOI: 10.1002/sim.7247
• Languages: English
• ISSNs: 0277-6715

Abstract

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 transfers 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. Copyright © 2017 John Wiley & Sons, Ltd.