The Statistical Challenge of Analysing Changes in Dual Energy Computed Tomography (DECT) Urate Volumes in People with Gout

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aut.relation.articlenumber152303
aut.relation.journalSemin Arthritis Rheum
aut.relation.startpage152303
aut.relation.volume63
dc.contributor.authorStewart, Sarah
dc.contributor.authorGamble, Greg
dc.contributor.authorDoyle, Anthony J
dc.contributor.authorSon, Chang-Nam
dc.contributor.authorAati, Opetaia
dc.contributor.authorLatto, Kieran
dc.contributor.authorHorne, Anne
dc.contributor.authorStamp, Lisa K
dc.contributor.authorDalbeth, Nicola
dc.date.accessioned2023-11-21T22:02:09Z
dc.date.available2023-11-21T22:02:09Z
dc.date.issued2023-11-02
dc.description.abstractBACKGROUND: Dual energy computed tomography (DECT) allows direct visualization of monosodium urate crystal deposition in gout. However, DECT urate volume data are often highly skewed (mostly small volumes with the remainder considerably larger), making statistical analyses challenging in longitudinal research. The aim of this study was to explore the ability of various analysis methods to normalise DECT urate volume data and determine change in DECT urate volumes over time. METHODS: Simulated datasets containing baseline and year 1 DECT urate volumes for 100 people with gout were created from two randomised controlled trials. Five methods were used to transform the DECT urate volume data prior to analysis: log-transformation, Box-Cox transformation, log(X-(min(X)-1)) transformation; inverse hyperbolic sine transformation, and rank order. Linear regression analyses were undertaken to determine the change in DECT urate volume between baseline and year 1. Cohen's d were calculated as a measure of effect size for each data treatment method. These analyses were then tested in a validation clinical trial dataset containing baseline and year 1 DECT urate volumes from 91 people with gout. RESULTS: No data treatment method successfully normalised the distribution of DECT urate volumes. For both simulated and validation data sets, significant reductions in DECT urate volumes were observed between baseline and Year 1 across all data treatment methods and there were no significant differences in Cohen's d effect sizes. CONCLUSIONS: Normalising highly skewed DECT urate volume data is challenging. Adopting commonly used transformation techniques may not significantly improve the ability to determine differences in measures of central tendency when comparing the change in DECT urate volumes over time.
dc.identifier.citationSemin Arthritis Rheum, ISSN: 0049-0172 (Print); 1532-866X (Online), Elsevier BV, 63, 152303-. doi: 10.1016/j.semarthrit.2023.152303
dc.identifier.doi10.1016/j.semarthrit.2023.152303
dc.identifier.issn0049-0172
dc.identifier.issn1532-866X
dc.identifier.urihttp://hdl.handle.net/10292/16975
dc.languageeng
dc.publisherElsevier BV
dc.relation.urihttps://www.sciencedirect.com/science/article/pii/S0049017223001452
dc.rights© 2023 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
dc.rights.accessrightsOpenAccess
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectDual-energy computed tomography
dc.subjectGout
dc.subjectStatistical analysis
dc.subjectUrate
dc.subject32 Biomedical and Clinical Sciences
dc.subject3202 Clinical Sciences
dc.subjectClinical Trials and Supportive Activities
dc.subjectClinical Research
dc.subject1103 Clinical Sciences
dc.subject1117 Public Health and Health Services
dc.subjectArthritis & Rheumatology
dc.subject3202 Clinical sciences
dc.titleThe Statistical Challenge of Analysing Changes in Dual Energy Computed Tomography (DECT) Urate Volumes in People with Gout
dc.typeJournal Article
pubs.elements-id530066
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