Rose et al. Respond to “G-Computation and Standardization in Epidemiology”American Journal of Epidemiology (2011)
AbstractWe thank Vansteelandt and Keiding (1) for their commentary on our article (2), in which we implemented G-computation, a maximum likelihood-based substitution estimator of the G-formula. The goals of that article included 1) translating G-computation into the applied epidemiology literature by using a point-treatment example and marginal parameter, 2) drawing connections between traditional regression and G-computation, 3) demonstrating G-computation in a simple simulated data set, and 4) briefly presenting related topics, such as super learning (3, 4). Their commentary provides valuable background on G-computation that was outside the scope of our article. Standardization was addressed, albeit briefly, in our article, and we disagree that our chosen presentation of G-computation was divorced from the literature. We respond to their remaining commentary via a road map for effect estimation (4), which can be a useful component of epidemiologic analysis and can guide investigators to address issues raised by Vansteelandt and Keiding (1).
Citation InformationSherri Rose, Jonathan M. Snowden and Kathleen M. Mortimer. "Rose et al. Respond to “G-Computation and Standardization in Epidemiology”" American Journal of Epidemiology Vol. 173 (2011)
Available at: http://works.bepress.com/sherri_rose/16/