This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 84
Type: Contributed
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Section on Statistics in Epidemiology
Abstract - #306907
Title: Efforts to Adjust for Confounding by Neighborhood Using Complex Survey Data
Author(s): Babette Brumback*+ and Amy Dailey and Zhulin He and Lyndia C. Brumback and Melvin Livingston
Companies: University of Florida and University of Florida and University of Florida and University of Washington and University of Florida
Address: PO Box 100231, Gainesville, FL, 32610-0231,
Keywords: confounding ; conditional logistic regression ; generalized linear mixed models ; misspecified mixing distribution ; complex survey data ; causal inference
Abstract:

In social epidemiology, one often considers neighborhood or contextual effects on health outcomes, in addition to effects of individual exposures. This talk is concerned with estimation of an individual exposure effect in the presence of confounding by neighborhood effects, motivated by an analysis of National Health Interview Survey (NHIS) data. In the analysis, we operationalize neighborhood as the secondary sampling unit of the survey, which consists of small groups of neighboring census blocks. Thus the neighborhoods are sampled with unequal probabilities, as are individuals within neighborhoods. We develop and compare several statistical methods for the analysis of the effect of an individual-level covariate on a binary outcome, adjusting for the high-dimensional categorical neighborhood identifier. Theory, simulations, and analyses of NIHS data are presented.


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