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Activity Number: 644
Type: Contributed
Date/Time: Thursday, August 13, 2015 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract #315035
Title: A Bayesian Hierarchical Model with Novel Prior Specifications for Estimating HIV Testing Rate
Author(s): Qian An* and Jian Kang and Ruiguang Song and Irene Hall
Companies: CDC and Emory University and CDC and CDC
Keywords: HIV testing rate ; Bayesian hierarchical model ; Temporal dependence ; Adaptive rejection metropolis sampling
Abstract:

We propose a Bayesian hierarchical model with two levels of hierarchy to estimate the HIV testing rate using annual AIDS and HIV diagnoses data. At level one, we model the latent numbers of HIV infections for each year using a Poisson distribution with the intensity parameter representing the HIV incidence rate. At level two, the annual number of AIDS and HIV diagnosed cases and all undiagnosed cases stratified by the HIV infections at different years are modeled using a multinomial distribution with parameters including the HIV testing rate. We propose a new class of prior for the HIV incidence rate and HIV testing rate taking into account the temporal dependence of these parameters to improve the estimation accuracy. We develop an efficient posterior computation algorithm based on the adaptive rejection sampling technique. We demonstrate our model using simulation studies.


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

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