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Performance of the Modified Poisson Regression Approach for Estimating Relative Risks From Clustered Prospective Data.
MedLine Citation:
PMID:  21841157     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
Modified Poisson regression, which combines a log Poisson regression model with robust variance estimation, is a useful alternative to log binomial regression for estimating relative risks. Previous studies have shown both analytically and by simulation that modified Poisson regression is appropriate for independent prospective data. This method is often applied to clustered prospective data, despite a lack of evidence to support its use in this setting. The purpose of this article is to evaluate the performance of the modified Poisson regression approach for estimating relative risks from clustered prospective data, by using generalized estimating equations to account for clustering. A simulation study is conducted to compare log binomial regression and modified Poisson regression for analyzing clustered data from intervention and observational studies. Both methods generally perform well in terms of bias, type I error, and coverage. Unlike log binomial regression, modified Poisson regression is not prone to convergence problems. The methods are contrasted by using example data sets from 2 large studies. The results presented in this article support the use of modified Poisson regression as an alternative to log binomial regression for analyzing clustered prospective data when clustering is taken into account by using generalized estimating equations.
Authors:
Lisa N Yelland; Amy B Salter; Philip Ryan
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-8-12
Journal Detail:
Title:  American journal of epidemiology     Volume:  -     ISSN:  1476-6256     ISO Abbreviation:  -     Publication Date:  2011 Aug 
Date Detail:
Created Date:  2011-8-15     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  7910653     Medline TA:  Am J Epidemiol     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
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