JSM 2005 - Toronto

Abstract #302736

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 204
Type: Invited
Date/Time: Tuesday, August 9, 2005 : 8:30 AM to 10:20 AM
Sponsor: IMS
Abstract - #302736
Title: A Nonlinear Hierarchical Model for Estimating Prevalence Rates with Small Samples
Author(s): Xiao-Li Meng*+ and Margarita Alegria and Chih-nan Chen and Jingchen Liu
Companies: Harvard University and Cambridge Health Alliance and Cambridge Health Alliance and Harvard University
Address: Deaprtment of Statistics, Cambridge, MA, 02138, U.S.A.
Keywords: Non-linear Hierarchical Model ; prevalence rates ; small sample ; psychiatric disorders ; National Latino and Asian American Study ; missing data
Abstract:

Estimating prevalence rates with small samples, especially for rare disease, is a challenging task. We encountered such a situation in the recent National Latino and Asian American Study (NLAAS) when assessing prevalence rates of psychiatric disorders. Due to small sizes of the samples in various age groups and the low prevalence of some disorders, the standard designed-based estimators are highly variable. Bayesian hierarchical modeling offers a more reliable approach by incorporating our knowledge on the smoothness of the prevalence rates as a function of age. However, the nonlinear nature of this function, compounded with zero observed rates or missing data, presents intricate modeling issues, such as sensitivity to the link function (for converting a rate parameter onto the real line). In this talk, we will report our findings and strategies we adopted to combat such problems.

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Revised March 2005