Testing and Estimation of Social Network Dependence With Time to Event Data. Issue 530 (2nd April 2020)

• Record Type: Journal Article
• Authors: Su, Lin; Lu, Wenbin; Song, Rui; Huang, Danyang
• Is Part Of: Journal of the American Statistical Association. Volume 115:Issue 530(2020)
• Page Start: 570
• Page End: 582
• Publication Date: 2020-04-02
• Subjects: Cox model -- EM algorithm -- Social network dependence -- Time-to-event data; Statistics -- Periodicals; Statistics -- Periodicals; Statistiques -- Périodiques; États-Unis -- Statistiques -- Périodiques; 519.5
• DOI: 10.1080/01621459.2019.1617153
• Languages: English
• ISSNs: 0162-1459

Abstract

Abstract: Nowadays, events are spread rapidly along social networks. We are interested in whether people's responses to an event are affected by their friends' characteristics. For example, how soon will a person start playing a game given that his/her friends like it? Studying social network dependence is an emerging research area. In this work, we propose a novel latent spatial autocorrelation Cox model to study social network dependence with time-to-event data. The proposed model introduces a latent indicator to characterize whether a person's survival time might be affected by his or her friends' features. We first propose a score-type test for detecting the existence of social network dependence. If it exists, we further develop an EM-type algorithm to estimate the model parameters. The performance of the proposed test and estimators are illustrated by simulation studies and an application to a time-to-event dataset about playing a popular mobile game from one of the largest online social network platforms. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.