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Relational event models for longitudinal network data with an application to interhospital patient transfers
Author(s) -
Vu Duy,
Lomi Alessandro,
Mascia Daniele,
Pallotti Francesca
Publication year - 2017
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.7247
Subject(s) - computer science , data mining , event (particle physics) , relational model , relational database , multinomial logistic regression , time point , econometrics , machine learning , mathematics , physics , philosophy , aesthetics , quantum mechanics
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.