TY - JOUR
T1 - The successes and challenges of harmonising juvenile idiopathic arthritis (JIA) datasets to create a large-scale JIA data resource
AU - CLUSTER consortium
AU - Lawson-Tovey, Saskia
AU - Smith, Samantha Louise
AU - Geifman, Nophar
AU - Shoop-Worrall, Stephanie
AU - Ng, Sandra
AU - Barnes, Michael R.
AU - Wedderburn, Lucy R.
AU - Hyrich, Kimme L.
AU - Kartawinata, Melissa
AU - Wanstall, Zoe
AU - Jebson, Bethany R.
AU - McNeece, Alyssia
AU - Ralph, Elizabeth
AU - Alexiou, Vasiliki
AU - Dekaj, Fatjon
AU - Kimonyo, Aline
AU - Merali, Fatema
AU - Sumner, Emma
AU - Robinson, Emily
AU - Feilding, Freya L.
AU - Dick, Andrew
AU - Beresford, Michael W.
AU - Carlsson, Emil
AU - Fairlie, Joanna
AU - Gritzfeld, Jenna F.
AU - Ramanan, Athimalaipet
AU - Duerr, Teresa
AU - Eyre, Stephen
AU - Raychaudhuri, Soumya
AU - Morris, Andrew
AU - Yarwood, Annie
AU - Smith, Samantha
AU - Bowes, John
AU - Martin, Paul
AU - Tordoff, Melissa
AU - Stadler, Michael
AU - Thomson, Wendy
AU - Tarasek, Damian
AU - Wallace, Chris
AU - Lin, Wei Yu
AU - Clarke, Sarah
AU - Kent, Toby
AU - Sornasse, Thierry
AU - Dastros-Pitei, Daniela
AU - Mukherjee, Sumanta
AU - Roberts, Jacqui
AU - Kallala, Rami
AU - Neale, Helen
AU - Ioannou, John
AU - Al-Mossawi, Hussein
N1 - Publisher Copyright:
© 2023, The Author(s).
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
KW - Children and young people
KW - Data harmonisation
KW - JIA
UR - http://www.scopus.com/inward/record.url?scp=85164540915&partnerID=8YFLogxK
U2 - 10.1186/s12969-023-00839-2
DO - 10.1186/s12969-023-00839-2
M3 - Article
C2 - 37438749
AN - SCOPUS:85164540915
SN - 1546-0096
VL - 21
JO - Pediatric Rheumatology
JF - Pediatric Rheumatology
IS - 1
M1 - 70
ER -