Back to Search
Start Over
Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: An example of smoking cessation
- Source :
-
Journal of Substance Abuse Treatment . Jul2013, Vol. 45 Issue 1, p99-108. 10p. - Publication Year :
- 2013
-
Abstract
- 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 07405472
- Volume :
- 45
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of Substance Abuse Treatment
- Publication Type :
- Academic Journal
- Accession number :
- 87463439
- Full Text :
- https://doi.org/10.1016/j.jsat.2013.01.005