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Activity Number: 42
Type: Contributed
Date/Time: Sunday, July 31, 2016 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract #320418 View Presentation
Title: A Novel Model for Binary Data Analysis
Author(s): Linbo Wang* and Thomas Richardson and James Robins
Companies: University of Washington and University of Washington and Harvard
Keywords: Bivariate mapping ; Estimating equation ; Semi-parametric model ; Variation independence
Abstract:

A common problem in formulating models for the relative risk and risk difference is the variation dependence between these parameters and the baseline risk, which is a nuisance model. We address this problem by proposing the conditional log odds-product as a preferred nuisance model. This novel nuisance model facilitates maximum-likelihood estimation, but also permits doubly-robust estimation for the parameters of interest. Our approach is illustrated via simulations and a data analysis.


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

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