Researchers in rheumatology should avoid categorization of continuous predictor variables

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

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Published in:BMC medical research methodology 2023-04, Vol.23 (1), p.104-104, Article 104
Main Authors: Salis, Zubeyir, Gallego, Blanca, Sainsbury, Amanda
Format: Article
Language:English
Subjects:
Arthritis
Body Mass Index
Categorization 1
Covariate 4
Diagnosis
Dichotomization 2
Evaluation
Hip joint
Humans
Knee
Medical research
Medicine, Experimental
Observational studies
Osteoarthritis
Pain
Predictor Variable 3
Radiography
Regression analysis
Rheumatic diseases
Rheumatology
Risk factors
Variables
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creator Salis, Zubeyir
Gallego, Blanca
Sainsbury, Amanda
description Rheumatology researchers often categorize continuous predictor variables. We aimed to show how this practice may alter results from observational studies in rheumatology. 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,
doi_str_mv 10.1186/s12874-023-01926-4
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We aimed to show how this practice may alter results from observational studies in rheumatology. 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, &lt; 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. 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). Categorization of continuous predictor variables alters the results of analyses and could lead to different conclusions; therefore, researchers in rheumatology should avoid it.</description><identifier>ISSN: 1471-2288</identifier><identifier>EISSN: 1471-2288</identifier><identifier>DOI: 10.1186/s12874-023-01926-4</identifier><identifier>PMID: 37101144</identifier><language>eng</language><publisher>England: BioMed Central Ltd</publisher><subject>Arthritis ; Body Mass Index ; Categorization 1 ; Covariate 4 ; Diagnosis ; Dichotomization 2 ; Evaluation ; Hip joint ; Humans ; Knee ; Medical research ; Medicine, Experimental ; Observational studies ; Osteoarthritis ; Pain ; Predictor Variable 3 ; Radiography ; Regression analysis ; Rheumatic diseases ; Rheumatology ; Risk factors ; Variables</subject><ispartof>BMC medical research methodology, 2023-04, Vol.23 (1), p.104-104, Article 104</ispartof><rights>2023. 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We aimed to show how this practice may alter results from observational studies in rheumatology. 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, &lt; 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. 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We aimed to show how this practice may alter results from observational studies in rheumatology. 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, &lt; 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. 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). Categorization of continuous predictor variables alters the results of analyses and could lead to different conclusions; therefore, researchers in rheumatology should avoid it.</abstract><cop>England</cop><pub>BioMed Central Ltd</pub><pmid>37101144</pmid><doi>10.1186/s12874-023-01926-4</doi><tpages>1</tpages><orcidid>https://orcid.org/0000-0001-9176-1574</orcidid><orcidid>https://orcid.org/0000-0003-2982-4591</orcidid><oa>free_for_read</oa></addata></record>
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subjects Arthritis
Body Mass Index
Categorization 1
Covariate 4
Diagnosis
Dichotomization 2
Evaluation
Hip joint
Humans
Knee
Medical research
Medicine, Experimental
Observational studies
Osteoarthritis
Pain
Predictor Variable 3
Radiography
Regression analysis
Rheumatic diseases
Rheumatology
Risk factors
Variables
title Researchers in rheumatology should avoid categorization of continuous predictor variables
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