Please use this identifier to cite or link to this item: https://doi.org/10.1080/03610918.2012.744044
Title: Analyzing binary outcome data with small clusters: A simulation study
Authors: Xu, Y. 
Lee, C.F.
Cheung, Y.B. 
Keywords: Binary outcome data
Generalized estimating equation
Random-effects logistic regression
Small clusters
Standard logistic regression
Within-cluster- resampling method
Issue Date: 1-Jan-2014
Source: Xu, Y., Lee, C.F., Cheung, Y.B. (2014-01-01). Analyzing binary outcome data with small clusters: A simulation study. Communications in Statistics: Simulation and Computation 43 (7) : 1771-1782. ScholarBank@NUS Repository. https://doi.org/10.1080/03610918.2012.744044
Abstract: Binary outcome data with small clusters often arise in medical studies and the size of clusters might be informative of the outcome. The authors conducted a simulation study to examine the performance of a range of statistical methods. The simulation results showed that all methods performed mostly comparable in the estimation of covariate effects. However, the standard logistic regression approach that ignores the clustering encountered an undercoverage problem when the degree of clustering was nontrivial. The performance of random-effects logistic regression approach tended to be affected by low disease prevalence, relatively small cluster size, or informative cluster size. © 2014 Copyright Taylor and Francis Group, LLC.
Source Title: Communications in Statistics: Simulation and Computation
URI: http://scholarbank.nus.edu.sg/handle/10635/109931
ISSN: 03610918
DOI: 10.1080/03610918.2012.744044
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