The statistical challenge of analysing changes in dual energy computed tomography (DECT) urate volumes in people with gout.
Dual-energy computed tomography
Gout
Statistical analysis
Urate
Journal
Seminars in arthritis and rheumatism
ISSN: 1532-866X
Titre abrégé: Semin Arthritis Rheum
Pays: United States
ID NLM: 1306053
Informations de publication
Date de publication:
Dec 2023
Dec 2023
Historique:
received:
30
06
2023
revised:
12
10
2023
accepted:
31
10
2023
pubmed:
9
11
2023
medline:
9
11
2023
entrez:
8
11
2023
Statut:
ppublish
Résumé
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. 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. 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. 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.
Sections du résumé
BACKGROUND
BACKGROUND
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
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
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
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.
Identifiants
pubmed: 37939600
pii: S0049-0172(23)00145-2
doi: 10.1016/j.semarthrit.2023.152303
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
152303Informations de copyright
Copyright © 2023. Published by Elsevier Inc.
Déclaration de conflit d'intérêts
Declaration of Competing Interest Lisa Stamp reports grants or contracts from Health Research Council of New Zealand, personal royalties or licenses from UptoDate, and consulting fees from Pharmac. Nicola Dalbeth reports grants or contracts from Health Research Council of New Zealand and Novotech, personal consulting fees from AstraZeneca, Dyve Biosciences, Horizon, Selecta, Arthrosi, JW Pharmaceutical Corporation, PK Med, PTC Therapeutics, Protalix, Cello Health, JPI, Unlocked Labs, and LG, payment or honoraria from Novartis and Hikma. The other authors declare no conflicts of interest.