Home 2010 - Volume 10(2) Handling Overdispersion in Mortality Data in Time-Series Epidemiologic Research Using SAS Software
Handling Overdispersion in Mortality Data in Time-Series Epidemiologic Research Using SAS Software PDF Print E-mail
Friday, 11 February 2011 09:44

Wan Rozita WM, Rasimah A, Mazrura S, Lim KH, Thana S


Analysis of count event data such as mortality cases, were often modelled using Poisson regression model. Maximum likelihood procedures were used by using SAS software to estimate the model parameters of a Poisson regression model.  However, the Negative Binomial distribution has been widely suggested as the alternative to the Poisson when there is proof of overdispersion phenomenon. We modelled the mortality cases as the dependent variable using Poisson and Negative Binomial regression and compare both of the models. The procedures were done in SAS by using the function PROC GENMOD. The results showed that the mortality data in Poisson regression exhibit large ratio values between deviance to degree of freedom which indicate model misspecification or overdispersion. This large ratio was found to be reduced in Negative Binomial regression. The Normal probability plot of Pearson residual confirmed that the Negative Binomial regression is a better model than Poisson regression in modelling the mortality data. The objective of this study is to compare the goodness of fit of Poisson regression model and Negative Binomial regression model in the application of air pollution epidemiologic time series study by using SAS software.

Key words: count data, Poisson regression, PROC GENMOD, SAS

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Last Updated on Sunday, 13 February 2011 03:51


ISSN No : 1675-0306
e-ISSN No : 2590-3829

"This is an Open Access Journal"

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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