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Dichotomisation using a distributional approach when the outcome is skewed

BACKGROUND: Dichotomisation of continuous outcomes has been rightly criticised by statisticians because of the loss of information incurred. However to communicate a comparison of risks, dichotomised outcomes may be necessary. Peacock et al. developed a distributional approach to the dichotomisation...

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Autores principales: Sauzet, Odile, Ofuya, Mercy, Peacock, Janet L
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4422142/
https://www.ncbi.nlm.nih.gov/pubmed/25902850
http://dx.doi.org/10.1186/s12874-015-0028-8
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author Sauzet, Odile
Ofuya, Mercy
Peacock, Janet L
author_facet Sauzet, Odile
Ofuya, Mercy
Peacock, Janet L
author_sort Sauzet, Odile
collection PubMed
description BACKGROUND: Dichotomisation of continuous outcomes has been rightly criticised by statisticians because of the loss of information incurred. However to communicate a comparison of risks, dichotomised outcomes may be necessary. Peacock et al. developed a distributional approach to the dichotomisation of normally distributed outcomes allowing the presentation of a comparison of proportions with a measure of precision which reflects the comparison of means. Many common health outcomes are skewed so that the distributional method for the dichotomisation of continuous outcomes may not apply. METHODS: We present a methodology to obtain dichotomised outcomes for skewed variables illustrated with data from several observational studies. We also report the results of a simulation study which tests the robustness of the method to deviation from normality and assess the validity of the newly developed method. RESULTS: The review showed that the pattern of dichotomisation was varying between outcomes. Birthweight, Blood pressure and BMI can either be transformed to normal so that normal distributional estimates for a comparison of proportions can be obtained or better, the skew-normal method can be used. For gestational age, no satisfactory transformation is available and only the skew-normal method is reliable. The normal distributional method is reliable also when there are small deviations from normality. CONCLUSIONS: The distributional method with its applicability for common skewed data allows researchers to provide both continuous and dichotomised estimates without losing information or precision. This will have the effect of providing a practical understanding of the difference in means in terms of proportions.
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spelling pubmed-44221422015-05-07 Dichotomisation using a distributional approach when the outcome is skewed Sauzet, Odile Ofuya, Mercy Peacock, Janet L BMC Med Res Methodol Research Article BACKGROUND: Dichotomisation of continuous outcomes has been rightly criticised by statisticians because of the loss of information incurred. However to communicate a comparison of risks, dichotomised outcomes may be necessary. Peacock et al. developed a distributional approach to the dichotomisation of normally distributed outcomes allowing the presentation of a comparison of proportions with a measure of precision which reflects the comparison of means. Many common health outcomes are skewed so that the distributional method for the dichotomisation of continuous outcomes may not apply. METHODS: We present a methodology to obtain dichotomised outcomes for skewed variables illustrated with data from several observational studies. We also report the results of a simulation study which tests the robustness of the method to deviation from normality and assess the validity of the newly developed method. RESULTS: The review showed that the pattern of dichotomisation was varying between outcomes. Birthweight, Blood pressure and BMI can either be transformed to normal so that normal distributional estimates for a comparison of proportions can be obtained or better, the skew-normal method can be used. For gestational age, no satisfactory transformation is available and only the skew-normal method is reliable. The normal distributional method is reliable also when there are small deviations from normality. CONCLUSIONS: The distributional method with its applicability for common skewed data allows researchers to provide both continuous and dichotomised estimates without losing information or precision. This will have the effect of providing a practical understanding of the difference in means in terms of proportions. BioMed Central 2015-04-24 /pmc/articles/PMC4422142/ /pubmed/25902850 http://dx.doi.org/10.1186/s12874-015-0028-8 Text en © Sauzet et al.; licensee BioMed Central. 2015 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. 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
Ofuya, Mercy
Peacock, Janet L
Dichotomisation using a distributional approach when the outcome is skewed
title Dichotomisation using a distributional approach when the outcome is skewed
title_full Dichotomisation using a distributional approach when the outcome is skewed
title_fullStr Dichotomisation using a distributional approach when the outcome is skewed
title_full_unstemmed Dichotomisation using a distributional approach when the outcome is skewed
title_short Dichotomisation using a distributional approach when the outcome is skewed
title_sort dichotomisation using a distributional approach when the outcome is skewed
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4422142/
https://www.ncbi.nlm.nih.gov/pubmed/25902850
http://dx.doi.org/10.1186/s12874-015-0028-8
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