On Modeling and Estimation for the Relative Risk and Risk Difference

Thomas S. Richardson, James M. Robins and Linbo Wang

Journal of the American Statistical Association, 2017, vol. 112, issue 519, 1121-1130

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. An R package {\tt brm} implementing the proposed methods is available on CRAN. Supplementary materials for this article are available online.

Date: 2017
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Citations: View citations in EconPapers (7)

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DOI: 10.1080/01621459.2016.1192546

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