Building a composite score for patient self-report of flare in osteoarthritis: a comparison of methods with the Flare-OA-16 questionnaire

• Published in: Journal of clinical epidemiology 2024-10, Vol.174 (5), p.111467, Article 111467
• Main Authors: Queiroga, Fabiana, Epstein, Jonathan, Erpelding, Marie-Line, Soudant, Marc, King, Lauren, Spitz, Elisabeth, Maillefert, Jean-Francis, Fautrel, Bruno, Callahan, Leigh F., March, Lyn, Hunter, David J., Guillemin, Francis
• Format: Article
• Language: English
• Subjects: Composite score; Confirmatory factor analysis; Correlation coefficient; Correlation coefficients; Decision making; Factor analysis; Flare; Hip joint; Human health and pathology; Knee; Life Sciences; Logistic regression; Logit models; Maximum likelihood method; Osteoarthritis; Patients; Psychological aspects; Questionnaire; Questionnaires; Rasch model; Regression analysis; Regression models; Reproducibility; Rheumatology; Self report; Similarity; Software
• ISSN: 0895-4356; 1878-5921; 1878-5921
• DOI: 10.1016/j.jclinepi.2024.111467

This study aims to compare methods of constructing a composite score for the Flare-OA-16 self-reported questionnaire. Participants with knee and hip osteoarthritis (OA) completed a validated 16-item questionnaire assessing five domains of flare. Three estimation methods were compared: (i) second-order confirmatory factor analysis (CFA); (ii) logistic regression, according to the participant's self-report of flare (yes/no); and (iii) Rasch method, with weighted scores in each dimension. The distribution (floor effect [FF] and ceiling effect [CF]) were described and the known-group validity (by self-reported flare) tested by Wilcoxon rank-sum test. Similarity between the scores was analyzed by intraclass correlation coefficient (ICC) and their performance against self-report compared by areas under ROC curves (AUC). Intrascore test-retest reliability at 14 days was assessed by ICC. In a sample of 381 participants, 247 reported having a flare. CFA showed fit indices (comparative fit index [CFI] = 0.95; root mean square error of approximation [RMSEA] = 0.08) and estimated composite mean score = 4.33(SD = 2.85) (FF = 14.9%, CF = 0%). For the logistic regression estimation, the mean composite score was 6.48 (SD = 3.13) (FF = 0%; CF = 0%). With Rasch model, the mean composite score was 4.35 (SD = 2.60) (FF = 14.9%; CF = 0%). Similarity analysis indicated a greater concordance between CFA and Rasch scores (ICC = 0.98) than between logistic regression score and the two others (ICC = 0.88 with Rasch score and 0.90 with CFA score). The AUC indicated similar performance of all methods: logistic model (AUC = 0.89 [0.85–0.92]), CFA, and Rasch model (AUC = 0.86 [0.82–0.90]). The difference between groups was significant (P