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Abstract Details
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Activity Number:
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83
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Type:
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Contributed
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Date/Time:
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Sunday, July 29, 2012 : 4:00 PM to 5:50 PM
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Sponsor:
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Health Policy Statistics Section
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| Abstract - #306715 |
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Title:
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Regression Analysis of Anthropometry Data: A Simulation Study of a Two-Stage Estimator
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Author(s):
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Stuart Sweeney*+ and Kevin Konty
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Companies:
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University of California at Santa Barbara and New York City Department of Public Health and Mental Hygiene
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Address:
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Dept of Geography, Santa Barbara, CA, 93106-4060, United States
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Keywords:
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Quantile regression ;
Ordinal regression ;
Anthropometry ;
Public health ;
Heteroskedasticity ;
Simulation Study
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Abstract:
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Regression analysis of anthropometry data has a long history in public health research. Early work relied on conditional mean regression models, but given that most policy interest is in either the lower or upper tail of a distribution, recent studies have utilized either binary outcome regression (logistic or ordinal logistic) or quantile regression. If the errors of the index function underlying binary models have non-constant variance, it is well-known that parameter estimates are inconsistent. We present simulation results of a proposed two-stage estimator to adjust for heteroskedasticity of unknown form. The two-stage estimator appears to substantially reduce bias in both parameter estimates and predictive changes in prevalence.
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