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https://hdl.handle.net/2440/67271
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Type: | Journal article |
Title: | Performance of the modified poisson regression approach for estimating relative risks from clustered prospective data |
Author: | Yelland, L. Salter, A. Ryan, P. |
Citation: | American Journal of Epidemiology, 2011; 174(8):984-992 |
Publisher: | Oxford Univ Press Inc |
Issue Date: | 2011 |
ISSN: | 0002-9262 1476-6256 |
Statement of Responsibility: | Lisa N. Yelland, Amy B. Salter, and Philip Ryan |
Abstract: | 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. |
Keywords: | clinical trial clustered data cohort studies generalized estimating equation relative risk |
Rights: | © The Author 2011. Published by Oxford University Press on behalf of the Johns Hopkins Bloomberg School of Public Health. All rights reserved. |
DOI: | 10.1093/aje/kwr183 |
Published version: | http://dx.doi.org/10.1093/aje/kwr183 |
Appears in Collections: | Aurora harvest 5 Public Health publications |
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