This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 360
Type: Contributed
Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
Sponsor: Biometrics Section
Abstract - #309060
Title: Mixed Models for Repeated Zero-Inflated Counts from Smoking Cessation Data
Author(s): E. Paul Wileyto*+ and Yimei Li and Daniel F. Heitjan
Companies: University of Pennsylvania and University of Pennsylvania and University of Pennsylvania
Address: 3535 W. Market Street, Suite 4100, Philadelphia, PA, 19104, USA
Keywords: Latent Mixture ; zero-inflated poisson ; zero-inflated negative binomial ; Mixed Models ; Ramdom Effects

Raw Time-Line Follow-Back data from smoking cessation trials consist of daily counts of cigarettes smoked. Because complete abstinence is the goal, these counts tend to have an over-abundance of zeros, and are best represented as a two-component latent mixture. We used SAS NLMIXED to fit zero-inflated Poisson and negative binomial count models to data from a smoking cessation trial of the drug Bupropion, with a zero-inflated component represented by logistic regression. The model included a variance term for the negative binomial part, another for the logistic part, and it included a covariance term describing the relationship between the two variance components. The estimated covariance term was positive, and implied that those who were more likely to be abstinent at any given time were also smoking fewer cigarettes when they did smoke.

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