title: On doubly robust estimation in a semiparametric odds ratio model.
abstract: We consider the doubly robust estimation of the parameters in a semiparametric conditional odds ratio model. Our estimators are consistent and asymptotically normal in a union model that assumes either of two variation independent baseline functions is correctly modelled but not necessarily both. Furthermore, when either outcome has finite support, our estimators are semiparametric efficient in the union model at the intersection submodel where both nuisance functions models are correct. For general outcomes, we obtain doubly robust estimators that are nearly efficient at the intersection submodel. Our methods are easy to implement as they do not require the use of the alternating conditional expectations algorithm of Chen (2007).
authors.affiliation: Department of Biostatistics ,  Harvard School of Public Health ,  677 Huntington Avenue, Boston, Massachusetts 02115 ,  U.S.A. etchetge@hsph.harvard.edu robins@hsph.harvard.edu.
authors.forename: Eric J
authors.initials: EJ
authors.lastname: Tchetgen Tchetgen
authors.forename: James M
authors.initials: JM
authors.lastname: Robins
authors.forename: Andrea
authors.initials: A
authors.lastname: Rotnitzky