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Integrating Computerized Linguistic and Social Network Analyses to Capture Addiction Recovery Capital in an Online Community
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Assessing intervention effects in a randomized trial within a social network.

Shaina J Alexandria1, Michael G Hudgens2, Allison E Aiello3

  • 1Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.

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|November 26, 2021
PubMed
Summary

This study introduces randomization-based inference (RI) to analyze social network interventions, like social distancing, and their spillover effects on disease transmission. The methods accurately estimate intervention impacts within connected populations, even with interference.

Keywords:
causal inferenceeX-FLUinterferencesocial distancingspillover effectstochastic potential outcomes

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Area of Science:

  • Epidemiology
  • Social Network Analysis
  • Biostatistics

Background:

  • Social network studies offer unique insights into intervention effects beyond direct participants, known as peer or spillover effects.
  • Interference, where one person's treatment impacts another's outcome, is common in social networks and complicates causal inference.
  • Randomization-based inference (RI) provides a robust framework for causal inference in randomized studies, even with interference.

Purpose of the Study:

  • To apply randomization-based inference (RI) for estimating intervention effects in a social network context.
  • To assess the causal effect of a social distancing intervention on influenza-like-illness (ILI) transmission within a connected network of college students (eX-FLU trial).
  • To enable inference on the per-contact probability of ILI transmission, accounting for interference and heterogeneous treatment effects.

Main Methods:

  • Utilized randomization-based inference (RI) methods tailored for network data.
  • Applied the methods to data from the eX-FLU trial, a randomized study on social distancing and ILI transmission.
  • Evaluated the proposed RI methods through empirical simulation studies.

Main Results:

  • The developed RI approach successfully enabled inference on the social distancing intervention's effect on ILI transmission probability per contact.
  • The methods demonstrated capability in handling interference between connected individuals in the network.
  • The analysis accounted for potential variations in treatment effects across individuals.

Conclusions:

  • Randomization-based inference (RI) is a viable and effective method for causal inference in social network intervention studies with interference.
  • The study successfully applied RI to real-world data from the eX-FLU trial, providing insights into social distancing effectiveness.
  • The findings support the use of RI for understanding complex intervention dynamics in interconnected populations.