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Activity Number:
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156
- Health Policy Statistics Section Student Paper Award
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Type:
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Topic-Contributed
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Date/Time:
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Tuesday, August 10, 2021 : 10:00 AM to 11:50 AM
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Sponsor:
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Health Policy Statistics Section
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Abstract #317400
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Title:
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Social Distancing and COVID-19: Randomization Inference for a Structured Dose-Response Relationship
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Author(s):
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Bo Zhang* and Siyu Heng and Ting Ye and Dylan S. Small
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Companies:
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University of Pennsylvania and University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
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Keywords:
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COVID-19;
longitudinal studies;
randomization inference;
statistical matching;
dose-response relationship
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Abstract:
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Social distancing is widely acknowledged as an effective public health policy combating the novel coronavirus. But extreme social distancing has costs and it is not clear how much social distancing is needed to achieve public health effects. In this article, we develop a design-based framework to test the causal null hypothesis and make inference about the dose-response relationship between social distancing and COVID-19 related death toll and case numbers. We first discuss how to embed observational data with a time-independent, continuous treatment dose into an approximate randomized experiment, and develop a randomization-based procedure that tests if a structured dose-response relationship fits the data. We then generalize the design and testing procedure to accommodate a time-dependent treatment dose in a longitudinal setting. Finally, we apply the proposed design and testing procedures to investigate the effect of social distancing during the phased reopening in the United States on public health outcomes. We reject a causal null hypothesis (p-value < 0.001) and conduct extensive extensive secondary analyses investigating the dose-response relationship.
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Authors who are presenting talks have a * after their name.
