QOLP-03. MEASURING CHANGE IN HEALTH-RELATED QUALITY OF LIFE: THE ADDED VALUE OF ANALYSIS ON THE INDIVIDUAL PATIENT LEVEL IN GLIOMA PATIENTS IN CLINICAL DECISION MAKING

Abstract

Abstract BACKGROUND: Health-related quality of life (HRQoL) is an important outcome in glioma research, reflecting the impact of disease and treatment on a patient’s functioning and wellbeing. Data on changes in HRQoL scores provide important information for clinical decision-making, but different analytical methods may lead to different interpretations of the impact of treatment on HRQoL. This study aimed to study whether different methods to evaluate change in HRQoL result in different interpretations. Methods: HRQoL and sociodemographical/clinical data from 15 randomized clinical trials were combined. Change in HRQoL scores was analyzed: (1)at the group level, comparing mean changes in scale/item scores between treatment arms over time, (2)at the patient level per scale/item by calculating the percentage of patients that deteriorated, improved or remained stable on a scale/item per scale/item, and (3)at the individual patient level combining all scales/items. Results: Data were available for 3727 patients. At the group scale/item level (method 1), only the item ‘hair loss’ showed a significant and clinically relevant change (i.e. ≥10 points) over time, whereas change scores on the other scales/items showed a statistically significant change only (all p< .001, range in change score:0.1–6.2). Analyses on the patient level per scale (method 2) indicated that, while a large proportion of patients had stable HRQoL over time (range:27–84%), many patients deteriorated (range:6–43%) or improved (range:8–32%) on a specific scale/item. At the individual patient level (method 3), the majority of patients (86%) showed both deterioration and improvement, while only 1% of the patients remained stable on all scales. Conclusion: Different analytical methods of changes in HRQoL result in distinct interpretations of treatment effects, all of which may be relevant for clinical decision-making. Additional information about the joint impact of treatment on all outcomes may help patients and physicians to make the best treatment decision.