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Activity Number:
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99
- Causal Inference for Infectious Disease Outcomes: Interference, Contagion, and Networks
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Type:
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Invited
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Date/Time:
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Monday, July 31, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract #322128
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Title:
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Randomization inference with general interference and censoring
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Author(s):
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Wen Wei Loh* and Michael Hudgens
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Companies:
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UNC and UNC
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Keywords:
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Interference ;
Causal inference ;
SUTVA ;
Vaccine ;
Herd immunity ;
Spillover
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Abstract:
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Interference occurs between individuals when the treatment (or exposure) of one individual affects the outcome of another individual. Previous work on causal inference methods in the presence of interference has focused on the setting where a priori it is assumed there is "partial interference" in the sense that individuals can be partitioned into groups wherein there is no interference between individuals in different groups. In this talk we consider randomization-based inferential methods proposed by Bowers et al. (2012, 2016) that allow for more general interference structures in the context of randomized experiments. Extensions of the Bowers et al. approach are considered, including allowing for right censored outcomes. The methods are utilized to assess whether interference is present in data from a cholera vaccine trial of n=73,000 women and children in Matlab, Bangladesh.
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Authors who are presenting talks have a * after their name.
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