title: Relational event models for longitudinal network data with an application to interhospital patient transfers.
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.
authors.affiliation: Interdisciplinary Institute of Data Science, University of Italian Switzerland, Lugano, Switzerland.|Department of Mathematics and Statistics, University of Melbourne, Melbourne, Australia.
authors.forename: Duy
authors.identifier: 0000-0002-5699-2737
authors.initials: D
authors.lastname: Vu
authors.affiliation: Interdisciplinary Institute of Data Science, University of Italian Switzerland, Lugano, Switzerland.
authors.forename: Alessandro
authors.initials: A
authors.lastname: Lomi
authors.affiliation: Department of Management, University of Bologna, Italy.
authors.forename: Daniele
authors.initials: D
authors.lastname: Mascia
authors.affiliation: Centre for Business Network Analysis, University of Greenwick, London, U.K.
authors.forename: Francesca
authors.initials: F
authors.lastname: Pallotti
mesh_terms: D056808:Biostatistics; D003627:Data Interpretation, Statistical; D006764:Hospitals, Community / Q000706:statistics & numerical data; D006801:Humans; D007558:Italy; D016015:Logistic Models; D008137:Longitudinal Studies; D015233:Models, Statistical; D010360:Patient Transfer / Q000706:statistics & numerical data*
keywords: interhospital patient transfers; interorganizational relations; relational event models; social network analysis