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A distributional approach to obtain adjusted comparisons of proportions of a population at risk
BACKGROUND: Dichotomisation of continuous data has statistical drawbacks such as loss of power but may be useful in epidemiological research to define high risk individuals. METHODS: We extend a methodology for the presentation of comparison of proportions derived from a comparison of means for a co...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897957/ https://www.ncbi.nlm.nih.gov/pubmed/27279891 http://dx.doi.org/10.1186/s12982-016-0050-2 |
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author | Sauzet, Odile Breckenkamp, Jürgen Borde, Theda Brenne, Silke David, Matthias Razum, Oliver Peacock, Janet L. |
author_facet | Sauzet, Odile Breckenkamp, Jürgen Borde, Theda Brenne, Silke David, Matthias Razum, Oliver Peacock, Janet L. |
author_sort | Sauzet, Odile |
collection | PubMed |
description | BACKGROUND: Dichotomisation of continuous data has statistical drawbacks such as loss of power but may be useful in epidemiological research to define high risk individuals. METHODS: We extend a methodology for the presentation of comparison of proportions derived from a comparison of means for a continuous outcome to reflect the relationship between a continuous outcome and covariates in a linear (mixed) model without losing statistical power. The so called “distributional method” is described and using perinatal data for illustration, results from the distributional method are compared to those of logistic regression and to quantile regression for three different outcomes. RESULTS: Estimates obtained using the distributional method for the comparison of proportions are consistently more precise than those obtained using logistic regression. For one of the three outcomes the estimates obtained from the distributional method and from logistic regression disagreed highlighting that the relationships between outcome and covariate differ conceptually between the two models. CONCLUSION: When an outcome follows the required condition of distribution shift between exposure groups, the results of a linear regression model can be followed by the corresponding comparison of proportions at risk. This dual approach provides more precise estimates than logistic regression thus avoiding the drawback of the usual dichotomisation of continuous outcomes. |
format | Online Article Text |
id | pubmed-4897957 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-48979572016-06-09 A distributional approach to obtain adjusted comparisons of proportions of a population at risk Sauzet, Odile Breckenkamp, Jürgen Borde, Theda Brenne, Silke David, Matthias Razum, Oliver Peacock, Janet L. Emerg Themes Epidemiol Research Article BACKGROUND: Dichotomisation of continuous data has statistical drawbacks such as loss of power but may be useful in epidemiological research to define high risk individuals. METHODS: We extend a methodology for the presentation of comparison of proportions derived from a comparison of means for a continuous outcome to reflect the relationship between a continuous outcome and covariates in a linear (mixed) model without losing statistical power. The so called “distributional method” is described and using perinatal data for illustration, results from the distributional method are compared to those of logistic regression and to quantile regression for three different outcomes. RESULTS: Estimates obtained using the distributional method for the comparison of proportions are consistently more precise than those obtained using logistic regression. For one of the three outcomes the estimates obtained from the distributional method and from logistic regression disagreed highlighting that the relationships between outcome and covariate differ conceptually between the two models. CONCLUSION: When an outcome follows the required condition of distribution shift between exposure groups, the results of a linear regression model can be followed by the corresponding comparison of proportions at risk. This dual approach provides more precise estimates than logistic regression thus avoiding the drawback of the usual dichotomisation of continuous outcomes. BioMed Central 2016-06-07 /pmc/articles/PMC4897957/ /pubmed/27279891 http://dx.doi.org/10.1186/s12982-016-0050-2 Text en © The Author(s) 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Sauzet, Odile Breckenkamp, Jürgen Borde, Theda Brenne, Silke David, Matthias Razum, Oliver Peacock, Janet L. A distributional approach to obtain adjusted comparisons of proportions of a population at risk |
title | A distributional approach to obtain adjusted comparisons of proportions of a population at risk |
title_full | A distributional approach to obtain adjusted comparisons of proportions of a population at risk |
title_fullStr | A distributional approach to obtain adjusted comparisons of proportions of a population at risk |
title_full_unstemmed | A distributional approach to obtain adjusted comparisons of proportions of a population at risk |
title_short | A distributional approach to obtain adjusted comparisons of proportions of a population at risk |
title_sort | distributional approach to obtain adjusted comparisons of proportions of a population at risk |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4897957/ https://www.ncbi.nlm.nih.gov/pubmed/27279891 http://dx.doi.org/10.1186/s12982-016-0050-2 |
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