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Activity Number: 507
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Statistical Consulting
Abstract - #304246
Title: Zero-Inflated Mixed Regression to Predict Self-Reported Frequency of Problems with Female Condoms Use Among Women at High Risk of Sexually Transmitted Disease
Author(s): Resmi Gupta*+ and Rhonda D Vandyke and Maurizio Macaluso
Companies: Cincinnati Children's Hospital Medical Center and Cincinnati Children's Hospital Medical Center and Cincinnati Children's Hospital Medical Center
Address: 3333 Burnet Avenue, Cincinnati, OH, 45249, United States
Keywords: zero-inflated Poisson mixed ; repeated measure count data ; generalized linear mixed models

Repeated measures data analysis involving zero-inflation often poses implementation and interpretability challenges to both statisticians and biomedical researchers. In addition to accounting for intra-subject correlation, the issue of excessive zero-counts must be addressed. Modeling count data with extra zeros with standard zero-inflated Poisson (ZIP) models may be inappropriate in such settings. A ZIP mixed regression model with random effects (ZIP-MR) allows specification of correlation and zero-count structures. We illustrate our approach using an epidemiologic study to evaluate self-reported frequency of problems with condom use in a group of women at high risk for contracting sexually transmitted disease who participated in follow up study of the impact of condom promotion intervention. A number of women reported no problems with use, resulting in zero-counts. ZIP-MR model performance was compared to standard ZIP using fit statistics (Akaike and Bayesian information criteria) and the likelihood ratio test (LRT); ZIP-MR model provided substantially better fit than the ZIP model based on model fit statistics.

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