Related Experiment Video
Updated: Jul 23, 2025

An Adoptive Transfer Model of Rheumatoid Arthritis in Mice
Published on: June 6, 2025
The successes and challenges of harmonising juvenile idiopathic arthritis (JIA) datasets to create a large-scale JIA
Saskia Lawson-Tovey1,2, Samantha Louise Smith3, Nophar Geifman4
1Centre for Genetics and Genomics Versus Arthritis, The University of Manchester, Manchester, UK. saskia.lawson-tovey@manchester.ac.uk.
Background:
CLUSTER is a UK consortium focussed on precision medicine research in JIA/JIA-Uveitis. As part of this programme, a large-scale JIA data resource was created by harmonizing and pooling existing real-world studies. Here we present challenges and progress towards creation of this unique large JIA dataset.
Methods:
Four real-world studies contributed data; two clinical datasets of JIA patients starting first-line methotrexate (MTX) or tumour necrosis factor inhibitors (TNFi) were created. Variables were selected based on a previously developed core dataset, and encrypted NHS numbers were used to identify children contributing similar data across multiple studies.
Results:
Of 7013 records (from 5435 individuals), 2882 (1304 individuals) represented the same child across studies. The final datasets contain 2899 (MTX) and 2401 (TNFi) unique patients; 1018 are in both datasets. Missingness ranged from 10 to 60% and was not improved through harmonisation.
Conclusions:
Combining data across studies has achieved dataset sizes rarely seen in JIA, invaluable to progressing research. Losing variable specificity and missingness, and their impact on future analyses requires further consideration.
Related Concept Videos
Genome-wide Association Studies-GWAS
GWAS does not require the identification of the target gene involved in...
The JAK-STAT Signaling Pathway
Rheumatic Heart Disease II: Clinical Manifestations and Diagnostic Studies
Autoimmune Disorders
Concept and Mechanism of Autoimmune Diseases
The immune...
Rheumatic Heart Disease I: Introduction
Statistical Software for Data Analysis and Clinical Trials

