JSM 2011 Online Program

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Abstract Details

Activity Number: 653
Type: Topic Contributed
Date/Time: Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #300662
Title: Using a Birth-Death Process to Account for Reporting Errors in Longitudinal Self-Reported Counts of Behavior
Author(s): Jihey Lee*+ and Robert E. Weiss and Marc Suchard
Companies: University of California at Los Angeles and University of California at Los Angeles and University of California at Los Angeles
Address: Department of Biostatistics,, Los Angeles, CA, 90095,
Keywords: Bayesian data analysis ; Poisson random effects model ; Recall error ; Sexual behaviors ; Stochastic process
Abstract:

We analyze longitudinal self-reported counts of sexual partners in the past three months from youth living with HIV. In self-report survey, subjects are asked to recall counts of events such as the number of sexual partners in the past. Subjects with small counts of behaviors may report the exact number, whereas subjects with large counts may have difficulty recalling the exact number. Thus, self-reported counts are noisy, and misreporting induces errors in the count variable. As a naive method for analyzing self-reported counts, the Poisson random effects model treats the observed counts as true counts and reporting errors in the outcome variable are ignored. Inferences are therefore based on incorrect information, and may lead to conclusions unsupported by the data. We describe a Bayesian model for analyzing longitudinal self-reported count data that formally accounts for reporting error. We model reported counts conditional on underlying true counts using a linear birth-death process, and use the Poisson random effects model to model the underlying true counts. A regression version of our model can identify characteristics of subjects with greater or lesser reporting error.


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