ArticleJournal of the Royal Statistical Society. Series C, Applied statistics2013
Hierarchical longitudinal models of relationships in social networks.
Article in Journal of the Royal Statistical Society. Series C, Applied statistics, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed.
- Exploiting relationship directionality to enhance statistical modeling of peer-influence across social networks.Statistics in medicine · 2024Article
- Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.Research square · 2024Article
- Estimating the impact of physician risky-prescribing on the network structure underlying physician shared-patient relationships.Applied network science · 2024Article
- Estimating contact network properties by integrating multiple data sources associated with infectious diseases.Statistics in medicine · 2023Article
- Beyond cultural and geographical proximity: delving into the factors that influence the dynamics of academic relationships between students in higher education.Higher education · 2022Article
- Dynamic Network Prediction.Network science (Cambridge University Press) · 2020Article
- Quantitative multivariate analysis of dynamic multicellular morphogenic trajectories.Integrative biology : quantitative biosciences from nano to macro · 2015Article
- Using Retrospective Sampling to Estimate Models of Relationship Status in Large Longitudinal Social Networks.Computational statistics & data analysis · 2015Article
- Results from using a new dyadic-dependence model to analyze sociocentric physician networks.Social science & medicine (1982) · 2014Article
- The analysis of social network data: an exciting frontier for statisticians.Statistics in medicine · 2013Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Motivated by the need to understand the dynamics of relationship formation and dissolution over time in real-world social networks we develop a new longitudinal model for transitions in the relationship status of pairs of individuals ("dyads"). We first specify a model for the relationship status of a single dyad and then extend it to account for important inter-dyad dependencies (e.g., transitivity - "a friend of a friend is a friend") and heterogeneity. Model parameters are estimated using Bayesian analysis implemented via Markov chain Monte Carlo. We use the model to perform novel analyses of two diverse longitudinal friendship networks: an excerpt of the Teenage Friends and Lifestyle Study (a moderately sized network) and the Framingham Heart Study (FHS) (a large network).
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.