A0410
Title: Identifying key influencers using an egocentric network-based randomized design
Authors: Zhibing He - Yale University (United States) [presenting]
Abstract: Many public health interventions are implemented in settings where individuals are interconnected, and the intervention assigned to randomly selected individuals may also affect others within their network. Evaluating such interventions requires assessing both the effect of the intervention on those who receive it and the spillover effect on those connected to the treated individuals. With behavioral interventions, spillover effects can be heterogeneous in that certain individuals, due to their social connectedness and individual characteristics, are more likely to respond to the intervention and influence their peers' behaviors. Targeting these individuals can enhance the effectiveness of interventions in the population. An egocentric network-based randomized trial (ENRT) design is proposed, where a set of index participants is recruited from the population and randomly assigned to the treatment group while concurrently collecting outcome data on their untreated network members. In such a design, an estimator is developed to assess heterogeneous spillover effects and a testing method, the multiple comparison with best (MCB), to identify subgroups whose treatment exhibits the largest spillover effect on their network members.
