On Modeling and Estimation for the Relative Risk and Risk Difference

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 nuis...

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Published in:Journal of the American Statistical Association 2017-07, Vol.112 (519), p.1121-1130
Main Authors: Richardson, Thomas S., Robins, James M., Wang, Linbo
Format: Article
Language:English
Subjects:
Bivariate mapping
Estimating equation
Semiparametric model
Theory and Methods
Variation independence
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description 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. An R package implementing the proposed methods is available on CRAN. Supplementary materials for this article are available online.
doi_str_mv 10.1080/01621459.2016.1192546
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source JSTOR Archival Journals and Primary Sources Collection; Taylor and Francis Science and Technology Collection
subjects Bivariate mapping
Estimating equation
Semiparametric model
Theory and Methods
Variation independence
title On Modeling and Estimation for the Relative Risk and Risk Difference
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