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Activity Number: 492
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Survey Research Methods Section
Abstract #319847 View Presentation
Title: Extension of the Peters-Belson Method to Estimate Health Disparities Among Multiple Groups Using Logistic Regression with Survey Data
Author(s): Yan Li* and Barry Graubard and Pengyu Huang and Joseph L. Gastwirth
Companies: Joint Program in Survey Methodology and National Cancer Institute and Fors Marsh Group and The George Washington University
Keywords: Taylor linearization ; complex survey data ; multinomial logistic regression ; proportional odds logistic ; unexplained disparity

Determining the extent of a disparity, if any, between groups of people, is of interest in many fields, including public health for medical treatment and prevention of disease. An observed difference in the mean outcome between an advantaged group (AG) and disadvantaged group (DG) can be due to differences in the distribution of relevant covariates. The Peters-Belson (PB) method fits a regression model with covariates to the AG t0 predict, for each DG member, their outcome measure as if they had been from the AG. The difference between the mean predicted and observed outcomes of DG members is the (unexplained) disparity of interest. We focus on applying the PB method to estimate the disparity based on logistic regression models using data collected from complex surveys with multiple DGs. Estimators of the unexplained disparity, an analytic variance estimator that is based on the Taylor linearization variance method, as well as a Wald test for testing a joint null hypothesis of zero for unexplained disparities between multiple minority groups and a majority group, are provided. Simulation studies and analyses of disparity in BMI in NHANES 1999-2004 are conducted.

Authors who are presenting talks have a * after their name.

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