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Identifying Key Influencers using an Egocentric Network-based Randomized Design

Abstract Number:

3715

Submission Type:

Contributed Abstract

Contributed Abstract Type:

Paper

Participants:

Zhibing He (1), Laura Forastiere (1)

Institutions:

(1) Yale University, N/A

Co-Author:

Laura Forastiere
Yale University

First Author:

Zhibing He
Yale University

Presenting Author:

Zhibing He
N/A

Abstract Text:

Many public health interventions are conducted in settings where individuals are connected to one another and the intervention assigned to randomly selected individuals may spill over to other individuals within their network.
Evaluating such interventions in spillover settings involves assessing both the average individual effect and spillover effect.
To estimate these effects, we propose an Egocentric Network-based Randomized Trial (ENRT) design, wherein a set of index participants is recruited from the population and randomly assigned to the treatment group.
Additionally, recognizing that certain individuals are more likely to influence their peers due to their social connectedness and their individual characteristics, intervening on these individuals can lead to more effective treatment strategies.
Multiple Comparison with the Best (MCB) is modified to identify key influencers by examining heterogeneity of the spillover effect.
The proposed methods are applied in a study of network-based peer HIV prevention education study, providing insights into strategies for selecting peer educators in peer education interventions.

Keywords:

Sponsors:

Health Policy Statistics Section

Tracks:

Miscellaneous

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