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Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: an example of smoking
Haiyi Xie1, Jill Tao, Gregory J McHugo
1Dartmouth Psychiatric Research Center, Department of Community and Family Medicine, Geisel School of Medicine at Dartmouth, Lebanon, NH 03766, USA. Haiyi.Xie@Dartmouth.Edu
Journal of Substance Abuse Treatment
|March 5, 2013
Summary
Zero-inflated models are popular for addiction research, but this study found simpler models like the negative binomial may fit count data with many zeros better. This is crucial for analyzing rare events or small count data.
Area of Science:
- Biostatistics
- Substance Abuse Research
- Longitudinal Data Analysis
Background:
- Count data with high zero-prevalence is common in addiction research.
- Zero-inflated and zero-adjusting models are frequently used for such data.
- The New Hampshire Dual Disorders Study provides a relevant dataset for this analysis.
Purpose of the Study:
- To review and compare five mixed-effects Poisson family models for count data with excess zeros.
- To evaluate the suitability of zero-inflated models versus simpler alternatives.
- To analyze a longitudinal outcome: number of smoking quit attempts.
Main Methods:
- Comparison of five mixed-effects Poisson family models.
- Analysis of longitudinal count data from the New Hampshire Dual Disorders Study.
- Evaluation of model fit for data with a high proportion of zeros.
Main Results:
- Count data with many zeros do not always necessitate zero-inflated models.
- Simpler models, such as the negative binomial model, can provide a better fit.
- Model selection depends on data characteristics like rare events or small means.
Conclusions:
- Zero-inflated models are not universally required for count data with excess zeros.
- The negative binomial model may be a more appropriate and simpler choice in certain scenarios.
- Careful consideration of data properties is essential for selecting the best statistical model.
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