Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count Responses
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 bo...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2012-01-01
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| Series: | AIDS Research and Treatment |
| Online Access: | http://dx.doi.org/10.1155/2012/593569 |
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| _version_ | 1832564140389957632 |
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| author | Yinglin Xia Dianne Morrison-Beedy Jingming Ma Changyong Feng Wendi Cross Xin Tu |
| author_facet | Yinglin Xia Dianne Morrison-Beedy Jingming Ma Changyong Feng Wendi Cross Xin Tu |
| author_sort | Yinglin Xia |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-b3674c2a3da5454fbede61f4e0d9b3da |
| institution | Kabale University |
| issn | 2090-1240 2090-1259 |
| language | English |
| publishDate | 2012-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | AIDS Research and Treatment |
| spelling | doaj-art-b3674c2a3da5454fbede61f4e0d9b3da2025-02-03T01:11:46ZengWileyAIDS Research and Treatment2090-12402090-12592012-01-01201210.1155/2012/593569593569Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count ResponsesYinglin Xia0Dianne Morrison-Beedy1Jingming Ma2Changyong Feng3Wendi Cross4Xin Tu5Department of Biostatistics and Computational Biology, Box 630, University of Rochester, 265 Crittenden Boulevard, Rochester, NY 14642, USACollege of Nursing, University of South Florida, 12901 Bruce B. Downs Boulevard, MDC22, Tampa, FL 33612, USADepartment of Biostatistics and Computational Biology, Box 630, University of Rochester, 265 Crittenden Boulevard, Rochester, NY 14642, USADepartment of Biostatistics and Computational Biology, Box 630, University of Rochester, 265 Crittenden Boulevard, Rochester, NY 14642, USADepartment of Psychiatry, University of Rochester, 300 Crittenden Boulevard, Rochester, NY 14642, USADepartment of Biostatistics and Computational Biology, Box 630, University of Rochester, 265 Crittenden Boulevard, Rochester, NY 14642, USAModeling 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.http://dx.doi.org/10.1155/2012/593569 |
| spellingShingle | Yinglin Xia Dianne Morrison-Beedy Jingming Ma Changyong Feng Wendi Cross Xin Tu Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count Responses AIDS Research and Treatment |
| title | Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count Responses |
| title_full | Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count Responses |
| title_fullStr | Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count Responses |
| title_full_unstemmed | Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count Responses |
| title_short | Modeling Count Outcomes from HIV Risk Reduction Interventions: A Comparison of Competing Statistical Models for Count Responses |
| title_sort | modeling count outcomes from hiv risk reduction interventions a comparison of competing statistical models for count responses |
| url | http://dx.doi.org/10.1155/2012/593569 |
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