کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
329745 | 543595 | 2013 | 10 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: An example of smoking cessation
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علم عصب شناسی
روانپزشکی بیولوژیکی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
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.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of Substance Abuse Treatment - Volume 45, Issue 1, July 2013, Pages 99–108
Journal: Journal of Substance Abuse Treatment - Volume 45, Issue 1, July 2013, Pages 99–108
نویسندگان
Haiyi Xie, Jill Tao, Gregory J. McHugo, Robert E. Drake,