Journal article
Longitudinal beta-binomial modeling using GEE for overdispersed binomial data
Statistics in medicine, Vol.36(6), pp.1029-1040
03/01/2017
DOI: 10.1002/sim.7191
PMID: 27917499
Abstract
Longitudinal binomial data are frequently generated from multiple questionnaires and assessments in various scientific settings for which the binomial data are often overdispersed. The standard generalized linear mixed effects model may result in severe underestimation of standard errors of estimated regression parameters in such cases and hence potentially bias the statistical inference. In this paper, we propose a longitudinal beta-binomial model for overdispersed binomial data and estimate the regression parameters under a probit model using the generalized estimating equation method. A hybrid algorithm of the Fisher scoring and the method of moments is implemented for computing the method. Extensive simulation studies are conducted to justify the validity of the proposed method. Finally, the proposed method is applied to analyze functional impairment in subjects who are at risk of Huntington disease from a multisite observational study of prodromal Huntington disease. Copyright (C) 2016 John Wiley & Sons, Ltd.
Details
- Title: Subtitle
- Longitudinal beta-binomial modeling using GEE for overdispersed binomial data
- Creators
- Hongqian Wu - University of IowaYing Zhang - Indiana University – Purdue University IndianapolisJeffrey D. Long - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Statistics in medicine, Vol.36(6), pp.1029-1040
- DOI
- 10.1002/sim.7191
- PMID
- 27917499
- NLM abbreviation
- Stat Med
- ISSN
- 0277-6715
- eISSN
- 1097-0258
- Publisher
- Wiley
- Number of pages
- 12
- Grant note
- 2 UL1 TR000442-06 / NIH grant; United States Department of Health & Human Services; National Institutes of Health (NIH) - USA
- Language
- English
- Date published
- 03/01/2017
- Academic Unit
- Psychiatry; Biostatistics
- Record Identifier
- 9984280830702771
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