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Assessing spillover effects: Handling missing outcomes in network-based studies.

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  • 1Division of Biostatistics, Department of Population Health, New York University Grossman School of Medicine, NY, USA.

Statistical Methods in Medical Research
|October 7, 2025
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Summary
This summary is machine-generated.

This study introduces a new method to estimate causal effects in social networks, even with missing outcome data. The method successfully reduced human immunodeficiency virus (HIV) risk behavior through community alerts and network spillover effects.

Keywords:
Causal inferenceHIV/AIDSdissemination/spilloverinterferenceinverse probability weightsnetwork studiespeople who inject drugs

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Area of Science:

  • Epidemiology
  • Social Network Analysis
  • Causal Inference

Background:

  • Estimating causal effects in social networks is complex due to spillover effects and missing outcome data (censoring).
  • Spillover effects occur when an intervention impacts individuals not directly exposed but connected within a network.

Purpose of the Study:

  • To develop and validate a novel statistical method for estimating causal effects in network-based studies with censored outcome data.
  • To assess the impact of community alerts on human immunodeficiency virus (HIV) risk behavior, considering both direct and spillover effects.

Main Methods:

  • Introduction of an inverse probability censoring weighted (IPCW) estimator, extending existing methods for network data with censoring.
  • Theoretical proofs of the estimator's consistency and asymptotic normality, with derivation of its asymptotic variance.
  • Application of the IPCW estimator to real-world data from the Transmission Reduction Intervention Project (TRIP) and simulation studies for performance evaluation.

Main Results:

  • The proposed IPCW estimator demonstrates consistency and asymptotic normality.
  • Simulation studies confirm the estimator's effectiveness with adequate sample sizes and network structures.
  • Analysis of the TRIP data revealed that community alerts significantly reduced HIV risk behavior among individuals and their network contacts.

Conclusions:

  • The developed IPCW method is a valuable tool for analyzing network data with censored outcomes.
  • Community alerts can effectively reduce HIV risk behavior through direct and indirect (spillover) pathways within social networks.
  • Interventions targeting social networks show promise for public health initiatives, like reducing HIV transmission risk.