The statistical challenge of analysing changes in dual energy computed tomography (DECT) urate volumes in people with gout
Authors' conclusion
Normalising highly skewed DECT urate volume data is challenging.
Study context
- Population
- People with gout with baseline and year 1 DECT urate volumes100 simulated
- Comparison
- Five data transformation methods (log, Box-Cox, log(X-(min(X)-1)), inverse hyperbolic sine, rank order) for analysing change in DECT urate volumes
- Outcome
- Ability to normalise DECT urate volume data and determine change in DECT urate volume (linear regression; Cohen's d effect size)
- Follow-up
- Year 1
Methodological detailssimulated datasets from two RCTs, validation clinical trial dataset
- Simulated datasets from two RCTs
- Validation clinical trial dataset
Performance
As reported in the abstract
- No data treatment method successfully normalised the distribution of DECT urate volumes
- Significant reductions in DECT urate volumes between baseline and Year 1 across all data treatment methods, simulated and validation data sets
- No significant differences in Cohen's d effect sizes
Original publication
The statistical challenge of analysing changes in dual energy computed tomography (DECT) urate volumes in people with gout. Semin Arthritis Rheum. Published 2 November 2023. doi:10.
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