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Activity Number: 358 - SPEED: Statistics in Epidemiology
Type: Contributed
Date/Time: Tuesday, August 1, 2017 : 10:30 AM to 11:15 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #325254
Title: Risk Ratios and Regression Estimates Under Contagion
Author(s): Olga Morozova* and Theodore Cohen and Forrest W Crawford
Companies: Yale University and Yale University and Yale School of Public Health
Keywords: risk ratio ; confounding ; Simpson's paradox ; transmission ; household epidemic ; infectious disease
Abstract:

Investigations of infectious diseases in cohort studies of clusters - households, villages, or small groups - often report risk ratios as a summary of the relationship between a binary covariate and outcome. Variants of Poisson regression are used for estimation of risk ratios when outcomes are binary. When the marginal outcome probability is correctly specified, modified Poisson regression can deliver consistent estimates of the risk ratio and robust standard errors, even when the correlation structure within clusters is unknown. Epidemiologists have warned that risk ratios may be biased when outcomes are contagious, but the nature and severity of this bias is not well understood. In this study, we assess the epidemiologic meaning of the risk ratio when outcomes are contagious by formulating a generic characterization of infectious disease transmission that includes possible transmission from an exogenous source. We exhibit analytically and by simulation the circumstances under which the estimated risk ratio can be seriously misleading. We explain these findings in the epidemiologic language of confounding and relate the directional bias to Simpson's paradox.


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

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