Evidence map›Paper›PMID 37101144›Full record

ArticleBMC medical research methodology2023

Researchers in rheumatology should avoid categorization of continuous predictor variables.

Zubeyir Salis, Blanca Gallego, Amanda Sainsbury

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Article in BMC medical research methodology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

5 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

3 authors.

Zubeyir SalisThe University of New South Wales, Centre for Big Data Research in Health, Kensington, NSW, Australia.ORCID 0000-0003-2982-4591
Blanca GallegoThe University of New South Wales, Centre for Big Data Research in Health, Kensington, NSW, Australia.
Amanda SainsburySchool of Human Sciences, The University of Western Australia, Crawley, Perth, WA, 6009, Australia. amanda.salis@uwa.edu.au.ORCID 0000-0001-9176-1574

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRheumatology researchers often categorize continuous predictor variables. We aimed to show how this practice may alter results from observational studies in rheumatology.

methodsWe 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.

resultsFor 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).

conclusionsCategorization of continuous predictor variables alters the results of analyses and could lead to different conclusions; therefore, researchers in rheumatology should avoid it.

Indexed as

RheumatologyBody Mass IndexHumansCategorization 1Covariate 4Dichotomization 2Predictor Variable 3

Identifiers

PMID37101144
PMCPMC10134601

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