IDEAS home Printed from https://ideas.repec.org/a/eee/socmed/v125y2015icp51-59.html
   My bibliography  Save this article

Reprint of: Results from using a new dyadic-dependence model to analyze sociocentric physician networks

Author

Listed:
  • Paul, Sudeshna
  • Keating, Nancy L.
  • Landon, Bruce E.
  • O’Malley, A. James

Abstract

Professional physician networks can potentially influence clinical practices and quality of care. With the current focus on coordinated care, discerning influences of naturally occurring clusters and other forms of dependence among physicians’ relationships based on their attributes and care patterns is an important area of research. In this paper, two directed physician networks: a physician influential conversation network (N = 33) and a physician network obtained from patient visit data (N = 135) are analyzed using a new model that accounts for effect modification of the within-dyad effect of reciprocity and inter-dyad effects involving three (or more) actors. The results from this model include more nuanced effects involving reciprocity and triadic dependence than under incumbent models and more flexible control for these effects in the extraction of other network phenomena, including the relationship between similarity of individuals’ attributes (e.g., same-gender, same residency location) and tie-status. In both cases we find extensive evidence of clustering and triadic dependence that if not accounted for confounds the effect of reciprocity and attribute homophily. Findings from our analysis suggest alternative conclusions to those from incumbent models.

Suggested Citation

  • Paul, Sudeshna & Keating, Nancy L. & Landon, Bruce E. & O’Malley, A. James, 2015. "Reprint of: Results from using a new dyadic-dependence model to analyze sociocentric physician networks," Social Science & Medicine, Elsevier, vol. 125(C), pages 51-59.
  • Handle: RePEc:eee:socmed:v:125:y:2015:i:c:p:51-59
    DOI: 10.1016/j.socscimed.2014.08.027
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0277953614005449
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.socscimed.2014.08.027?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Nicola J. Cooper & Paul C. Lambert & Keith R. Abrams & Alexander J. Sutton, 2007. "Predicting costs over time using Bayesian Markov chain Monte Carlo methods: an application to early inflammatory polyarthritis," Health Economics, John Wiley & Sons, Ltd., vol. 16(1), pages 37-56, January.
    2. Sudeshna Paul & A. James O'Malley, 2013. "Hierarchical longitudinal models of relationships in social networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(5), pages 705-722, November.
    3. Paola Zappa, 2011. "The network structure of knowledge sharing among physicians," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(5), pages 1109-1126, August.
    4. West, Elizabeth & Barron, David N. & Dowsett, Juliet & Newton, John N., 1999. "Hierarchies and cliques in the social networks of health care professionals: implications for the design of dissemination strategies," Social Science & Medicine, Elsevier, vol. 48(5), pages 633-646, March.
    5. Peter D. Hoff, 2005. "Bilinear Mixed-Effects Models for Dyadic Data," Journal of the American Statistical Association, American Statistical Association, vol. 100, pages 286-295, March.
    6. Stanley Wasserman & Philippa Pattison, 1996. "Logit models and logistic regressions for social networks: I. An introduction to Markov graphs andp," Psychometrika, Springer;The Psychometric Society, vol. 61(3), pages 401-425, September.
    7. Peter D. Hoff, 2009. "Multiplicative latent factor models for description and prediction of social networks," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 261-272, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Paul, Sudeshna & Keating, Nancy L. & Landon, Bruce E. & O'Malley, A. James, 2014. "Results from using a new dyadic-dependence model to analyze sociocentric physician networks," Social Science & Medicine, Elsevier, vol. 117(C), pages 67-75.
    2. O’Malley, A. James & Paul, Sudeshna, 2015. "Using retrospective sampling to estimate models of relationship status in large longitudinal social networks," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 35-46.
    3. De Nicola, Giacomo & Fritz, Cornelius & Mehrl, Marius & Kauermann, Göran, 2023. "Dependence matters: Statistical models to identify the drivers of tie formation in economic networks," Journal of Economic Behavior & Organization, Elsevier, vol. 215(C), pages 351-363.
    4. Sudeshna Paul & A. James O'Malley, 2013. "Hierarchical longitudinal models of relationships in social networks," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 62(5), pages 705-722, November.
    5. Andreas Dzemski, 2019. "An Empirical Model of Dyadic Link Formation in a Network with Unobserved Heterogeneity," The Review of Economics and Statistics, MIT Press, vol. 101(5), pages 763-776, December.
    6. Sosa, Juan & Betancourt, Brenda, 2022. "A latent space model for multilayer network data," Computational Statistics & Data Analysis, Elsevier, vol. 169(C).
    7. Patacchini, Eleonora & Hsieh, Chih-Sheng & Lin, Xu, 2019. "Social Interaction Methods," CEPR Discussion Papers 14141, C.E.P.R. Discussion Papers.
    8. Peter D. Hoff, 2009. "Multiplicative latent factor models for description and prediction of social networks," Computational and Mathematical Organization Theory, Springer, vol. 15(4), pages 261-272, December.
    9. Peter R. Herman, 2022. "Modeling complex network patterns in international trade," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 158(1), pages 127-179, February.
    10. Terrence D. Jorgensen & Aditi M. Bhangale & Yves Rosseel, 2024. "Two-Stage Limited-Information Estimation for Structural Equation Models of Round-Robin Variables," Stats, MDPI, vol. 7(1), pages 1-34, February.
    11. Paola Zappa, 2011. "The network structure of knowledge sharing among physicians," Quality & Quantity: International Journal of Methodology, Springer, vol. 45(5), pages 1109-1126, August.
    12. Tracy Sweet & Samrachana Adhikari, 2020. "A Latent Space Network Model for Social Influence," Psychometrika, Springer;The Psychometric Society, vol. 85(2), pages 251-274, June.
    13. Manuel E. Sosa & Steven D. Eppinger & Craig M. Rowles, 2004. "The Misalignment of Product Architecture and Organizational Structure in Complex Product Development," Management Science, INFORMS, vol. 50(12), pages 1674-1689, December.
    14. Cilem Selin Hazir & Corinne Autant-Bernard, 2012. "Using Affiliation Networks to Study the Determinants of Multilateral Research Cooperation Some empirical evidence from EU Framework Programs in biotechnology," Working Papers 1212, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    15. Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
    16. Samrachana Adhikari & Beau Dabbs, 2018. "Social Network Analysis in R: A Software Review," Journal of Educational and Behavioral Statistics, , vol. 43(2), pages 225-253, April.
    17. Joseph F. Levy & Marjorie A. Rosenberg, 2019. "A Latent Class Approach to Modeling Trajectories of Health Care Cost in Pediatric Cystic Fibrosis," Medical Decision Making, , vol. 39(5), pages 593-604, July.
    18. David Levinson & Arthur Huang, 2012. "A Positive Theory of Network Connectivity," Environment and Planning B, , vol. 39(2), pages 308-325, April.
    19. Lomi, Alessandro & Fonti, Fabio, 2012. "Networks in markets and the propensity of companies to collaborate: An empirical test of three mechanisms," Economics Letters, Elsevier, vol. 114(2), pages 216-220.
    20. McMahon, James M. & Pouget, Enrique R. & Tortu, Stephanie, 2006. "A guide for multilevel modeling of dyadic data with binary outcomes using SAS PROC NLMIXED," Computational Statistics & Data Analysis, Elsevier, vol. 50(12), pages 3663-3680, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:socmed:v:125:y:2015:i:c:p:51-59. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/315/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.