Abstract
In this issue of the Journal, Snowden et al. (Am J Epidemiol. 2011;173(7):731–738) give a didactic explanation of G-computation as an approach for estimating the causal effect of a point exposure. The authors of the present commentary reinforce the idea that their use of G-computation is equivalent to a particular form of model-based standardization, whereby reference is made to the observed study population, a technique that epidemiologists have been applying for several decades. They comment on the use of standardized versus conditional effect measures and on the relative predominance of the inverse probability-of-treatment weighting approach as opposed to G-computation. They further propose a compromise approach, doubly robust standardization, that combines the benefits of both of these causal inference techniques and is not more difficult to implement.
Originalsprog | Engelsk |
---|---|
Tidsskrift | American Journal of Epidemiology |
Vol/bind | 173 |
Udgave nummer | 7 |
Sider (fra-til) | 739-742 |
Antal sider | 4 |
ISSN | 0002-9262 |
DOI |
|
Status | Udgivet - 1 apr. 2011 |