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AIDS Res Treat. 2012; 2012: 593569.
Published online Mar 25, 2012. doi:  10.1155/2012/593569
PMCID: PMC3318204
Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count Responses
Yinglin Xia, 1 * Dianne Morrison-Beedy, 2 Jingming Ma, 1 Changyong Feng, 1 Wendi Cross, 3 and Xin Tu 1
1Department of Biostatistics and Computational Biology, Box 630, University of Rochester, 265 Crittenden Boulevard, Rochester, NY 14642, USA
2College of Nursing, University of South Florida, 12901 Bruce B. Downs Boulevard, MDC22, Tampa, FL 33612, USA
3Department of Psychiatry, University of Rochester, 300 Crittenden Boulevard, Rochester, NY 14642, USA
*Yinglin Xia: yinglin_xia/at/
Academic Editor: Christina Ramirez Kitchen
Received May 27, 2011; Revised December 13, 2011; Accepted January 14, 2012.
Modeling count data from sexual behavioral outcomes involves many challenges, especially when the data exhibit a preponderance of zeros and overdispersion. In particular, the popular Poisson log-linear model is not appropriate for modeling such outcomes. Although alternatives exist for addressing both issues, they are not widely and effectively used in sex health research, especially in HIV prevention intervention and related studies. In this paper, we discuss how to analyze count outcomes distributed with excess of zeros and overdispersion and introduce appropriate model-fit indices for comparing the performance of competing models, using data from a real study on HIV prevention intervention. The in-depth look at these common issues arising from studies involving behavioral outcomes will promote sound statistical analyses and facilitate research in this and other related areas.
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