JSM 2005 Online Program

Abstract #303949

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	516
Type:	Contributed
Date/Time:	Thursday, August 11, 2005 : 10:30 AM to 12:20 PM
Sponsor:	General Methodology
Abstract - #303949
Title:	Estimation of Covariate Balanced Contrasts of Expectations
Author(s):	David B. Nelson*+ and Siamak Noorbaloochi
Companies:	VA HSR/University of Minnesota and VA HSR/University of Minnesota
Address:	Minneapolis VA Medical Center 2E152, Minneapolis, MN, 55417, United States
Keywords:	Unbiased Estimation ; Dimension Reduction ; Propensity
Abstract:	Estimation of intervention effects in observational studies when the intervention populations are imbalanced with respect to observed covariates is a well-known problem. When the populations are imbalanced with respect to covariates, influencing the outcome defining intervention effects as contrasts of the simple conditional expectations is deficient. Covariance analysis and propensity theory are two approaches that have been developed to assess intervention effects in such situations. Covariance analysis can be hampered by inherent difficulties in model identification when the dimension of the covariate measures is large. Propensity theory was developed for bias reduction in the estimation of intervention effects for dichotomous population membership. A key feature of this approach is the reduction of the covariates to the one-dimensional propensity score. We consider a conditional density ratio approach to bias reduction in the estimation of covariate-balanced contrasts of expectations among multiple populations. We show that propensity of intervention assignment is a secondary consequence to a generalized notion of sufficient summaries, as discussed in Regression Graphics.

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