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Activity Number: 650 - Relaxing No Interference Assumptions in Clustered Randomized Trials
Type: Invited
Date/Time: Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
Sponsor: Social Statistics Section
Abstract #326760 Presentation
Title: IMP: Interference Manipulating Permutations
Author(s): Michael Baiocchi* and Eric Jay Daza
Companies: Stanford University and Stanford University
Keywords: interference; study design; causal inference; tightening; spillover; cluster randomized trial
Abstract:

This talk provides a framework for randomization when the intervention level for one unit of observation has the potential to impact other units' outcomes. The goal of interference manipulating permutations (IMP) is to incorporate knowledge of interference between units into the study design, improving the data quality in anticipation of using one of several forms of inference developed to obtain traditional causal estimates in the presence of interference. This approach may be of particular interest to investigators engaged in the prevention of infectious disease, as well as behavioral interventions.

The framework is motivated by two cluster-randomized trials (CRTs) of a sexual assault prevention program delivered in schools situated within the informal settlements of Nairobi, Kenya. Interviews collected from the pilot study indicated that the young girls felt motivated to share the skills gained from the intervention with their friends and family. IMP was developed and deployed for the formal CRT study of the intervention. This proposed framework draws upon earlier work by Moulton (2004 - Clinical Trials) and Tukey (1993 - Control Clin Trials).


Authors who are presenting talks have a * after their name.

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