Modeling count data in the addiction field: Some simple recommendations.

TitreModeling count data in the addiction field: Some simple recommendations.
Publication TypeJournal Article
Year of Publication2018
AuthorsBaggio, S, Iglesias, K, Rousson, V
JournalInternational journal of methods in psychiatric research
Volume27
Issue1
Pagination1-10
Date Published03/2018
DOI10.1002/mpr.1585
ISSN1557-0657
Mots-clésBiomedical Research/methods, Computer Simulation, coverage of confidence interval, Data Interpretation, guidelines, Humans, Models, Psychiatry/methods, simulation, Statistical, Statistical Distributions, substance use, type 1 error
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.

Alternate URL

http://www.ncbi.nlm.nih.gov/pubmed/29027305?dopt=Abstract

First publication date (online)

10/2017

WOS ID (UT)

000426505900008

Alternate JournalInt J Methods Psychiatr Res
Citation Key / SERVAL ID8280
Peer reviewRefereed
PubMed ID29027305

                         

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