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Finding influential subjects in a network using a causal framework.
Lee, Youjin; Buchanan, Ashley L; Ogburn, Elizabeth L; Friedman, Samuel R; Halloran, M Elizabeth; Katenka, Natallia V; Wu, Jing; Nikolopoulos, Georgios K.
Affiliation
  • Lee Y; Department of Biostatistics, Brown University, Providence, Rhode Island, USA.
  • Buchanan AL; Department of Pharmacy Practice, University of Rhode Island, Providence, Rhode Island, USA.
  • Ogburn EL; Department of Biostatistics, Johns Hopkins University, Baltimore, Maryland, USA.
  • Friedman SR; Department of Population Health, NYU Grossman School of Medicine, New York, New York, USA.
  • Halloran ME; Biostatistics, Bioinformatics, and Epidemiology Program, Vaccine and Infectious Disease Division, Fred Hutchinson Cancer Center, Seattle, Washington, USA.
  • Katenka NV; Department of Biostatistics, University of Washington, Seattle, Washington, USA.
  • Wu J; Department of Computer Science and Statistics, University of Rhode Island, Providence, Rhode Island, USA.
  • Nikolopoulos GK; Department of Computer Science and Statistics, University of Rhode Island, Providence, Rhode Island, USA.
Biometrics ; 79(4): 3715-3727, 2023 12.
Article in En | MEDLINE | ID: mdl-36788358
ABSTRACT
Researchers across a wide array of disciplines are interested in finding the most influential subjects in a network. In a network setting, intervention effects and health outcomes can spill over from one node to another through network ties, and influential subjects are expected to have a greater impact than others. For this reason, network research in public health has attempted to maximize health and behavioral changes by intervening on a subset of influential subjects. Although influence is often defined only implicitly in most of the literature, the operative notion of influence is inherently causal in many cases influential subjects are those we should intervene on to achieve the greatest overall effect across the entire network. In this work, we define a causal notion of influence using potential outcomes. We review existing influence measures, such as node centrality, that largely rely on the particular features of the network structure and/or on certain diffusion models that predict the pattern of information or diseases spreads through network ties. We provide simulation studies to demonstrate when popular centrality measures can agree with our causal measure of influence. As an illustrative example, we apply several popular centrality measures to the HIV risk network in the Transmission Reduction Intervention Project and demonstrate the assumptions under which each centrality can represent the causal influence of each participant in the study.
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Full text: 1 Database: MEDLINE Main subject: Computer Simulation Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Biometrics Year: 2023 Type: Article Affiliation country: United States

Full text: 1 Database: MEDLINE Main subject: Computer Simulation Type of study: Diagnostic_studies / Prognostic_studies Limits: Humans Language: En Journal: Biometrics Year: 2023 Type: Article Affiliation country: United States