%0 Journal Article %1 Xie2013 %A Xie, Haiyi %A Tao, Jill %A McHugo, Gregory J %A Drake, Robert E %D 2013 %J Journal of substance abuse treatment %K DataInterpretation Humans LongitudinalStudies Models NewHampshire PoissonDistribution ResearchDesign SmokingCessation SmokingCessation:statistics&numericaldata Statistical %N 1 %P 99-108 %R 10.1016/j.jsat.2013.01.005 %T Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: an example of smoking cessation. %U http://www.ncbi.nlm.nih.gov/pubmed/23453482 %V 45 %X Count data with skewness and many zeros are common in substance abuse and addiction research. Zero-adjusting models, especially zero-inflated models, have become increasingly popular in analyzing this type of data. This paper reviews and compares five mixed-effects Poisson family models commonly used to analyze count data with a high proportion of zeros by analyzing a longitudinal outcome: number of smoking quit attempts from the New Hampshire Dual Disorders Study. The findings of our study indicated that count data with many zeros do not necessarily require zero-inflated or other zero-adjusting models. For rare event counts or count data with small means, a simpler model such as the negative binomial model may provide a better fit. %@ 1873-6483; 0740-5472

@article{Xie2013, abstract = {Count data with skewness and many zeros are common in substance abuse and addiction research. Zero-adjusting models, especially zero-inflated models, have become increasingly popular in analyzing this type of data. This paper reviews and compares five mixed-effects Poisson family models commonly used to analyze count data with a high proportion of zeros by analyzing a longitudinal outcome: number of smoking quit attempts from the New Hampshire Dual Disorders Study. The findings of our study indicated that count data with many zeros do not necessarily require zero-inflated or other zero-adjusting models. For rare event counts or count data with small means, a simpler model such as the negative binomial model may provide a better fit.}, added-at = {2023-02-03T11:44:35.000+0100}, author = {Xie, Haiyi and Tao, Jill and McHugo, Gregory J and Drake, Robert E}, biburl = {https://www.bibsonomy.org/bibtex/2c882c0bd782cea4c7c117b4cbe040ed4/jepcastel}, city = {Dartmouth Psychiatric Research Center, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA. Haiyi.Xie@Dartmouth.Edu}, doi = {10.1016/j.jsat.2013.01.005}, interhash = {7eadc616b34320065f7ff01a2f788995}, intrahash = {c882c0bd782cea4c7c117b4cbe040ed4}, isbn = {1873-6483; 0740-5472}, issn = {1873-6483}, journal = {Journal of substance abuse treatment}, keywords = {DataInterpretation Humans LongitudinalStudies Models NewHampshire PoissonDistribution ResearchDesign SmokingCessation SmokingCessation:statistics&numericaldata Statistical}, month = {7}, note = {CI: Copyright (c) 2013; JID: 8500909; 2012/03/01 [received]; 2012/11/27 [revised]; 2013/01/22 [accepted]; 2013/02/28 [aheadofprint]; ppublish;}, number = 1, pages = {99-108}, pmid = {23453482}, timestamp = {2023-02-03T11:44:35.000+0100}, title = {Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: an example of smoking cessation.}, url = {http://www.ncbi.nlm.nih.gov/pubmed/23453482}, volume = 45, year = 2013 }