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.

Relational event models for longitudinal network data with an application to interhospital patient transfers / Vu, Duy; Lomi, Alessandro; Mascia, Daniele; Pallotti, Francesca. - In: STATISTICS IN MEDICINE. - ISSN 0277-6715. - 36:14(2017), pp. 2265-2287. [10.1002/sim.7247]

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

Mascia Daniele;
2017

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.
interhospital patient transfers; interorganizational relations; relational event models; social network analysis; Epidemiology; Statistics and Probability
Relational event models for longitudinal network data with an application to interhospital patient transfers / Vu, Duy; Lomi, Alessandro; Mascia, Daniele; Pallotti, Francesca. - In: STATISTICS IN MEDICINE. - ISSN 0277-6715. - 36:14(2017), pp. 2265-2287. [10.1002/sim.7247]
File in questo prodotto:
File Dimensione Formato  
Vu_et_al-2017-Statistics_in_Medicine.pdf

Solo gestori archivio

Tipologia: Versione dell'editore
Licenza: DRM non definito
Dimensione 2.3 MB
Formato Adobe PDF
2.3 MB Adobe PDF   Visualizza/Apri   Richiedi una copia
Pubblicazioni consigliate

Caricamento pubblicazioni consigliate

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11385/187654
Citazioni
  • Scopus 18
  • ???jsp.display-item.citation.isi??? 15
social impact