Back

Assessment of psychometric validity and cross-cultural differential item functioning of the Doleur Neuropatique en 4 questions interview and the pain domain of the Western Ontario and McMaster Universities Osteoarthritis Index

Laigaard, J. H.; Aljuboori, S. M. S.; Overgaard, S.; Christensen, K. B.

2025-09-25 orthopedics
10.1101/2025.09.24.25336543 medRxiv
Show abstract

BackgroundRandomised trials and meta-evidence increasingly rely on patient-reported outcome measures (PROMs). Many PROMs are applied in languages and settings that differ from the original target population. However, translation of PROMs poses a threat to their construct validity, including issues with cross-cultural adaptation ObjectiveThis study aims to assess the psychometric validity of the Danish versions of the WOMAC pain domain and the DN4i, including cross-cultural differential item functioning. MethodsThe study is based on a large dataset of 12-to 18-month pain outcomes after primary total hip arthroplasty (THA), total knee arthroplasty (TKA), or unicompartmental knee arthroplasty (UKA) for osteoarthritis (ClinicalTrials.gov identifiers NCT05845177 and NCT05900791). In addition to this dataset, we will apply for international data in similar populations to assess the cross-cultural differential item functioning. The assessed instruments are the 5-item Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) pain domain (Likert-scale, version 3.1) and the 7-item Doleur Neuropatique en 4 questions interview (DN4i) (Danish translation from www.mapi-trust.org). We will evaluate if the data fit a congeneric measurement model, i.e. a model that assumes that the set of observed items all measure the same underlying latent factor. This evaluation of construct validity is done using Item Response Theory (IRT) and Confirmatory Factor Analysis (CFA). PerspectiveThe results for WOMAC pain domain and DN4i will be reported in two separate reports, which are submitted for publication in peer-reviewed journals. We will seek to make the reports freely available, either by open-access publication or through publication on a preprint server, e.g. www.medrxiv.org.

Matching journals

The top 1 journal accounts for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.