Linking EORTC QLQ-C-30 and PedsQL/PEDQOL physical functioning scores in patients with osteosarcoma. (July 2022) - Budde, Axel; Baust, Katja; Weinhold, Leonie; Bernstein, Mark; Bielack, Stefan; Dhooge, Catharina; Hjorth, Lars; Janeway, Katherine A.; Jenney, Meriel; Krailo, Mark D.; Marina, Neyssa; Nagarajan, Rajaram; et al.
Abstract: Purpose: The available questionnaires for quality-of-life (QoL) assessments are age-group specific, limiting comparability and impeding longitudinal analyses. The comparability of measurements, however, is a necessary condition for gaining scientific evidence. To overcome this problem, we assessed the viability of harmonising data from paediatric and adult patient-reported outcome (PRO) measures. Method: To this end, we linked physical functioning scores from the Paediatric Quality of Life Inventory (PedsQL) and the Paediatric Quality of Life Questionnaire (PEDQOL) to the European Organisation for Research and Treatment of Cancer Core Questionnaire (EORTC QLQ-C30) for adults. Samples from the EURAMOS-1 QoL sub-study of 75 (PedsQL) and 112 (PEDQOL) adolescent osteosarcoma patients were concurrently administered both paediatric and adult questionnaires on 98 (PedsQL) and 156 (PEDQOL) occasions. We identified corresponding scores using the single-group equipercentile linking method. Results: Linked physical functioning scores showed sufficient concordance to the EORTC QLQ-C30: Lin's ρ = 0.74 (PedsQL) and Lin's ρ = 0.64 (PEDQOL). Conclusion: Score linking provides clinicians and researchers with a common metric for assessing QoL with PRO measures across the entire lifespan of patients. Highlights: Score linking is a promising method for linking physical functioning scores obtained with paediatric and adult quality-of-life (QoL) questionnaires in a population of survivors of childhood osteosarcoma. The method provides methodological rigour for quality-of-life (QoL) assessments across the lifespan of cancer patients when the use of distinct age-group-specific inventories is unavoidable. The approach may create the conditions for conducting longitudinal mixed-model meta-analyses.