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A cautionary note regarding count models of alcohol consumption in randomized controlled trials
BACKGROUND: Alcohol consumption is commonly used as a primary outcome in randomized alcohol treatment studies. The distribution of alcohol consumption is highly skewed, particularly in subjects with alcohol dependence. METHODS: In this paper, we will consider the use of count models for outcomes in...
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Formato: | Texto |
Lenguaje: | English |
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BioMed Central
2007
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810542/ https://www.ncbi.nlm.nih.gov/pubmed/17302984 http://dx.doi.org/10.1186/1471-2288-7-9 |
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author | Horton, Nicholas J Kim, Eugenia Saitz, Richard |
author_facet | Horton, Nicholas J Kim, Eugenia Saitz, Richard |
author_sort | Horton, Nicholas J |
collection | PubMed |
description | BACKGROUND: Alcohol consumption is commonly used as a primary outcome in randomized alcohol treatment studies. The distribution of alcohol consumption is highly skewed, particularly in subjects with alcohol dependence. METHODS: In this paper, we will consider the use of count models for outcomes in a randomized clinical trial setting. These include the Poisson, over-dispersed Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial. We compare the Type-I error rate of these methods in a series of simulation studies of a randomized clinical trial, and apply the methods to the ASAP (Addressing the Spectrum of Alcohol Problems) trial. RESULTS: Standard Poisson models provide a poor fit for alcohol consumption data from our motivating example, and did not preserve Type-I error rates for the randomized group comparison when the true distribution was over-dispersed Poisson. For the ASAP trial, where the distribution of alcohol consumption featured extensive over-dispersion, there was little indication of significant randomization group differences, except when the standard Poisson model was fit. CONCLUSION: As with any analysis, it is important to choose appropriate statistical models. In simulation studies and in the motivating example, the standard Poisson was not robust when fit to over-dispersed count data, and did not maintain the appropriate Type-I error rate. To appropriately model alcohol consumption, more flexible count models should be routinely employed. |
format | Text |
id | pubmed-1810542 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2007 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-18105422007-03-13 A cautionary note regarding count models of alcohol consumption in randomized controlled trials Horton, Nicholas J Kim, Eugenia Saitz, Richard BMC Med Res Methodol Research Article BACKGROUND: Alcohol consumption is commonly used as a primary outcome in randomized alcohol treatment studies. The distribution of alcohol consumption is highly skewed, particularly in subjects with alcohol dependence. METHODS: In this paper, we will consider the use of count models for outcomes in a randomized clinical trial setting. These include the Poisson, over-dispersed Poisson, negative binomial, zero-inflated Poisson and zero-inflated negative binomial. We compare the Type-I error rate of these methods in a series of simulation studies of a randomized clinical trial, and apply the methods to the ASAP (Addressing the Spectrum of Alcohol Problems) trial. RESULTS: Standard Poisson models provide a poor fit for alcohol consumption data from our motivating example, and did not preserve Type-I error rates for the randomized group comparison when the true distribution was over-dispersed Poisson. For the ASAP trial, where the distribution of alcohol consumption featured extensive over-dispersion, there was little indication of significant randomization group differences, except when the standard Poisson model was fit. CONCLUSION: As with any analysis, it is important to choose appropriate statistical models. In simulation studies and in the motivating example, the standard Poisson was not robust when fit to over-dispersed count data, and did not maintain the appropriate Type-I error rate. To appropriately model alcohol consumption, more flexible count models should be routinely employed. BioMed Central 2007-02-15 /pmc/articles/PMC1810542/ /pubmed/17302984 http://dx.doi.org/10.1186/1471-2288-7-9 Text en Copyright © 2007 Horton et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( (http://creativecommons.org/licenses/by/2.0) ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Horton, Nicholas J Kim, Eugenia Saitz, Richard A cautionary note regarding count models of alcohol consumption in randomized controlled trials |
title | A cautionary note regarding count models of alcohol consumption in randomized controlled trials |
title_full | A cautionary note regarding count models of alcohol consumption in randomized controlled trials |
title_fullStr | A cautionary note regarding count models of alcohol consumption in randomized controlled trials |
title_full_unstemmed | A cautionary note regarding count models of alcohol consumption in randomized controlled trials |
title_short | A cautionary note regarding count models of alcohol consumption in randomized controlled trials |
title_sort | cautionary note regarding count models of alcohol consumption in randomized controlled trials |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1810542/ https://www.ncbi.nlm.nih.gov/pubmed/17302984 http://dx.doi.org/10.1186/1471-2288-7-9 |
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