Modeling time-dependent overdispersion in longitudinal count data
Poisson regression is important in the analysis of longitudinal count data. However, the variance of responses is often much greater than the sample mean in practice, contradicting the Poisson model. To solve this overdispersion problem, negative binomial regression model was introduced by earlier researchers by adding another error term to the Poisson model. By default, the parameter of the additional error term, called the overdispersion parameter, is constant during the study period, but we find that it may fail in the research of epilepsy. Thus a formal likelihood ratio test is proposed and the test conforms that a time-dependent overdispersion phenomenon does exist. Then a mixed effect negative binomial model is proposed to take into account the time-dependent overdispersion, producing significantly better regression results compared with most earlier models. The proposed test and regression approach can easily be done by SAS PROC NLMIXED. The extensive simulation studies are conducted to evaluate the performance of the methods proposed.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 58 (2013)
Issue (Month): C ()
|Contact details of provider:|| Web page: http://www.elsevier.com/locate/csda|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Vandna Jowaheer, 2002. "Analysing longitudinal count data with overdispersion," Biometrika, Biometrika Trust, vol. 89(2), pages 389-399, June.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:257-264. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Dana Niculescu)
If references are entirely missing, you can add them using this form.