Testing and Estimation of Social Network Dependence With Time to Event Data.
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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.
SUBMITTER: Su L
PROVIDER: S-EPMC8130777 | biostudies-literature |
REPOSITORIES: biostudies-literature
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