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Researchers in rheumatology should avoid categorization of continuous predictor variables

BACKGROUND: Rheumatology researchers often categorize continuous predictor variables. We aimed to show how this practice may alter results from observational studies in rheumatology. METHODS: We conducted and compared the results of two analyses of the association between our predictor variable (per...

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Autores principales: Salis, Zubeyir, Gallego, Blanca, Sainsbury, Amanda
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134601/
https://www.ncbi.nlm.nih.gov/pubmed/37101144
http://dx.doi.org/10.1186/s12874-023-01926-4
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author Salis, Zubeyir
Gallego, Blanca
Sainsbury, Amanda
author_facet Salis, Zubeyir
Gallego, Blanca
Sainsbury, Amanda
author_sort Salis, Zubeyir
collection PubMed
description BACKGROUND: Rheumatology researchers often categorize continuous predictor variables. We aimed to show how this practice may alter results from observational studies in rheumatology. METHODS: We conducted and compared the results of two analyses of the association between our predictor variable (percentage change in body mass index [BMI] from baseline to four years) and two outcome variable domains of structure and pain in knee and hip osteoarthritis. These two outcome variable domains covered 26 different outcomes for knee and hip combined. In the first analysis (categorical analysis), percentage change in BMI was categorized as ≥ 5% decrease in BMI, < 5% change in BMI, and ≥ 5% increase in BMI, while in the second analysis (continuous analysis), it was left as a continuous variable. In both analyses (categorical and continuous), we used generalized estimating equations with a logistic link function to investigate the association between the percentage change in BMI and the outcomes. RESULTS: For eight of the 26 investigated outcomes (31%), the results from the categorical analyses were different from the results from the continuous analyses. These differences were of three types: 1) for six of these eight outcomes, while the continuous analyses revealed associations in both directions (i.e., a decrease in BMI had one effect, while an increase in BMI had the opposite effect), the categorical analyses showed associations only in one direction of BMI change, not both; 2) for another one of these eight outcomes, the categorical analyses suggested an association with change in BMI, while this association was not shown in the continuous analyses (this is potentially a false positive association); 3) for the last of the eight outcomes, the continuous analyses suggested an association of change in BMI, while this association was not shown in the categorical analyses (this is potentially a false negative association). CONCLUSIONS: Categorization of continuous predictor variables alters the results of analyses and could lead to different conclusions; therefore, researchers in rheumatology should avoid it. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01926-4.
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spelling pubmed-101346012023-04-28 Researchers in rheumatology should avoid categorization of continuous predictor variables Salis, Zubeyir Gallego, Blanca Sainsbury, Amanda BMC Med Res Methodol Research BACKGROUND: Rheumatology researchers often categorize continuous predictor variables. We aimed to show how this practice may alter results from observational studies in rheumatology. METHODS: We conducted and compared the results of two analyses of the association between our predictor variable (percentage change in body mass index [BMI] from baseline to four years) and two outcome variable domains of structure and pain in knee and hip osteoarthritis. These two outcome variable domains covered 26 different outcomes for knee and hip combined. In the first analysis (categorical analysis), percentage change in BMI was categorized as ≥ 5% decrease in BMI, < 5% change in BMI, and ≥ 5% increase in BMI, while in the second analysis (continuous analysis), it was left as a continuous variable. In both analyses (categorical and continuous), we used generalized estimating equations with a logistic link function to investigate the association between the percentage change in BMI and the outcomes. RESULTS: For eight of the 26 investigated outcomes (31%), the results from the categorical analyses were different from the results from the continuous analyses. These differences were of three types: 1) for six of these eight outcomes, while the continuous analyses revealed associations in both directions (i.e., a decrease in BMI had one effect, while an increase in BMI had the opposite effect), the categorical analyses showed associations only in one direction of BMI change, not both; 2) for another one of these eight outcomes, the categorical analyses suggested an association with change in BMI, while this association was not shown in the continuous analyses (this is potentially a false positive association); 3) for the last of the eight outcomes, the continuous analyses suggested an association of change in BMI, while this association was not shown in the categorical analyses (this is potentially a false negative association). CONCLUSIONS: Categorization of continuous predictor variables alters the results of analyses and could lead to different conclusions; therefore, researchers in rheumatology should avoid it. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-01926-4. BioMed Central 2023-04-26 /pmc/articles/PMC10134601/ /pubmed/37101144 http://dx.doi.org/10.1186/s12874-023-01926-4 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Salis, Zubeyir
Gallego, Blanca
Sainsbury, Amanda
Researchers in rheumatology should avoid categorization of continuous predictor variables
title Researchers in rheumatology should avoid categorization of continuous predictor variables
title_full Researchers in rheumatology should avoid categorization of continuous predictor variables
title_fullStr Researchers in rheumatology should avoid categorization of continuous predictor variables
title_full_unstemmed Researchers in rheumatology should avoid categorization of continuous predictor variables
title_short Researchers in rheumatology should avoid categorization of continuous predictor variables
title_sort researchers in rheumatology should avoid categorization of continuous predictor variables
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10134601/
https://www.ncbi.nlm.nih.gov/pubmed/37101144
http://dx.doi.org/10.1186/s12874-023-01926-4
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