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Poisson regression has been widely used to model count data. However, it is often criticized for its restrictive assumption of equi-dispersion, meaning equality between the variance and the mean. In real-life applications, count data often exhibits over-dispersion and excess zeroes. While Negative binomial regression is able to model count data with over-dispersion, both Hurdle (Mullahy, 1986) and Zero-inflated (Lambert, 1992) regressions address the issue of excess zeroes in their own rights. Different modeling strategies for count data and various statistical tests for model evaluation are illustrated through an example of healthcare utilization. The purpose of this paper is to provide by far the most complete survey of count data modeling strategy in SAS for the user group.

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Format:PDFSize:235 KB
Date:Mar 2008
Pages:12

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