On Modeling and Estimation for the Relative Risk and Risk Difference

Linbo Wang, Thomas Richardson, James Robins

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 brm implementing the proposed methods
is available on CRAN.