ArticleInternational journal of methods in psychiatric research2018
Modeling count data in the addiction field: Some simple recommendations.
Article in International journal of methods in psychiatric research, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- A Randomized Trial of Alcohol Telemedicine in Primary Care: Pharmacotherapy and Referral Outcomes.Journal of general internal medicine · 2026Trial
- Pay to Play or Pay to Lose? The Impact of In-game Spending in Digital Games on Gambling Problems.Journal of gambling studies · 2026Article
- Use of Cannabis Protective Behavioral Strategies During Pregnancy.Cannabis (Albuquerque, N.M.) · 2026Article
- Development and Validation of the Positive Outcomes of Cannabis Use Scale (POCUS) Among Predominantly White Adults in the United States.Assessment · 2025Article
- The impacts of stress and loneliness on gambling and gaming problems: A nationwide longitudinal study.The International journal of social psychiatry · 2024Article
- Longitudinal examination of alcohol use motives, item-level protective behavioral strategies, and alcohol-related consequences.Alcohol, clinical & experimental research · 2024Article
- The selection of statistical models for reporting count outcomes and intervention effects in brief alcohol intervention trials: A review and recommendations.Alcohol, clinical & experimental research · 2024Review
- Protocol for a mobile laboratory study of co-administration of cannabis concentrates with a standard alcohol dose in humans.PloS one · 2022Article
- Young adult use, dual use, and simultaneous use of alcohol and marijuana: An examination of differences across use status on marijuana use context, rates, and consequences.Psychology of addictive behaviors : journal of the Society of Psychologists in Addictive Behaviors · 2021Article
- The Role of Perceived Loneliness in Youth Addictive Behaviors: Cross-National Survey Study.JMIR mental health · 2020Article
- Models for analyzing zero-inflated and overdispersed count data: an application to cigarette and marijuana use.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2018Article
- Modeling count data in the addiction field: Some simple recommendations.International journal of methods in psychiatric research · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Analyzing count data is frequent in addiction studies but may be cumbersome, time-consuming, and cause misleading inference if models are not correctly specified. We compared different statistical models in a simulation study to provide simple, yet valid, recommendations when analyzing count data.We used 2 simulation studies to test the performance of 7 statistical models (classical or quasi-Poisson regression, classical or zero-inflated negative binomial regression, classical or heteroskedasticity-consistent linear regression, and Mann-Whitney test) for predicting the differences between population means for 9 different population distributions (Poisson, negative binomial, zero- and one-inflated Poisson and negative binomial, uniform, left-skewed, and bimodal). We considered a large number of scenarios likely to occur in addiction research: presence of outliers, unbalanced design, and the presence of confounding factors. In unadjusted models, the Mann-Whitney test was the best model, followed closely by the heteroskedasticity-consistent linear regression and quasi-Poisson regression. Poisson regression was by far the worst model. In adjusted models, quasi-Poisson regression was the best model. If the goal is to compare 2 groups with respect to count data, a simple recommendation would be to use quasi-Poisson regression, which was the most generally valid model in our extensive simulations.
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