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Evaluation via simulation of statistical corrections for network nonindependence.

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  • 1RAND Corporation, 20 Park Plaza #920, Boston, MA 02216, USA.

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|December 15, 2025
PubMed
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Analyzing social influence in health services requires accounting for network interdependencies. Random sampling of network nodes showed the lowest false positive rates but reduced statistical power, indicating a need for improved methods.

Keywords:
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Area of Science:

  • Social epidemiology
  • Health services research
  • Network analysis

Background:

  • Social processes and context significantly influence health behaviors.
  • Social influence within networks, such as among healthcare providers, can shape health outcomes.
  • Statistical methods often assume data independence, which is violated in social networks.

Purpose of the Study:

  • To systematically compare commonly used statistical methods for addressing network non-independence due to social influence.
  • To evaluate the performance of these methods regarding false positive rates, coefficient bias, and statistical power.

Main Methods:

  • Simulated network data were generated to mimic social influence.
  • Eight statistical methods for accounting for network non-independence were compared.
  • Performance metrics included false positive rates, unbiased coefficient estimates, and statistical power.

Main Results:

  • None of the tested methods achieved the nominal 0.05 false positive rate.
  • Random sampling of network nodes demonstrated the lowest false positive rates.
  • The random sampling method resulted in reduced statistical power.

Conclusions:

  • Current methods for addressing network non-independence in health services research have limitations.
  • Further methodological development is necessary to balance accuracy and power in analyzing social influence.
  • Accurate analysis of health-related social influence requires robust statistical approaches.