Tchetgen on "Doubly robust estimation in a semi-parametric odds ratio model" 

 This Wednesday, October 28th, the Applied Statistics workshop will welcome  Eric Tchetgen Tchetgen , Assistant Professor of Epidemiology at Harvard School of Public Health, presenting his work titled "Doubly robust estimation in a semi-parametric odds ratio model." Eric has provided the following abstract for the paper: 

 
We consider the doubly robust estimation of the parameters in a semi-parametric conditional odds ratio model characterizing the effect of an exposure in the presence of many confounders. We develop estimators that are consistent and asymptotically normal in a union model where either a prospective baseline density function or a retrospective baseline density function is correctly specified but not necessarily both. The case of a binary outcome is of particular interest, then our approach yields a doubly robust locally efficient estimator in a semi-parametric logistic regression model   For general types of outcomes, we provide a strategy to obtain doubly robust estimators that are nearly locally efficient   We illustrate the method in a simulation study and an application in statistical genetics. Finally, we briefly discuss extensions of the proposed method to the semi-parametric estimation of a parameter indexing an interaction between two exposures on the logistic scale, as well as extensions to the setting of a time-varying exposure in the presence of time-varying confounding.
 

 The Applied Statistics workshop meets each Wednesday in room K-354, CGIS-Knafel (1737 Cambridge St). We start at 12 noon with a light lunch, with presentations beginning around 12:15 and we usually wrap up around 1:30 pm. We hope you can make it.