Dataset Information

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

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.

SUBMITTER: Baggio S

PROVIDER: S-EPMC6877188 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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

Baggio Stéphanie S Iglesias Katia K Rousson Valentin V

International journal of methods in psychiatric research 20171013 1

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 heteroskedastic ...[more]

PMID: 29027305

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Project description:Shotgun proteomics provides the most powerful analytical platform for global inventory of complex proteomes using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and allows a global analysis of protein changes. Nevertheless, sampling of complex proteomes by current shotgun proteomics platforms is incomplete, and this contributes to variability in assessment of peptide and protein inventories by spectral counting approaches. Thus, shotgun proteomics data pose challenges in comparing proteomes from different biological states. We developed an analysis strategy using quasi-likelihood Generalized Linear Modeling (GLM), included in a graphical interface software package (QuasiTel) that reads standard output from protein assemblies created by IDPicker, an HTML-based user interface to query shotgun proteomic data sets. This approach was compared to four other statistical analysis strategies: Student t test, Wilcoxon rank test, Fisher's Exact test, and Poisson-based GLM. We analyzed the performance of these tests to identify differences in protein levels based on spectral counts in a shotgun data set in which equimolar amounts of 48 human proteins were spiked at different levels into whole yeast lysates. Both GLM approaches and the Fisher Exact test performed adequately, each with their unique limitations. We subsequently compared the proteomes of normal tonsil epithelium and HNSCC using this approach and identified 86 proteins with differential spectral counts between normal tonsil epithelium and HNSCC. We selected 18 proteins from this comparison for verification of protein levels between the individual normal and tumor tissues using liquid chromatography-multiple reaction monitoring mass spectrometry (LC-MRM-MS). This analysis confirmed the magnitude and direction of the protein expression differences in all 6 proteins for which reliable data could be obtained. Our analysis demonstrates that shotgun proteomic data sets from different tissue phenotypes are sufficiently rich in quantitative information and that statistically significant differences in proteins spectral counts reflect the underlying biology of the samples.

Dataset Information

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

Publications

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

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