期刊
STATISTICS IN MEDICINE
卷 36, 期 14, 页码 2265-2287出版社
WILEY
DOI: 10.1002/sim.7247
关键词
social network analysis; relational event models; interorganizational relations; interhospital patient transfers
类别
资金
- European Science Foundation (ECRPVI Program)
- Swiss National Science Foundation [133271]
- Australian Research Council Discovery Project [DP120102902]
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 (C) 2017 John Wiley & Sons, Ltd.
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