OBJECTIVES : Relapsed/refractory multiple myeloma (RRMM) is a progressive, incurable cancer that has become nonresponsive or progressed following several lines of treatment. Novel therapies under investigation for treatment of RRMM, such as chimeric antigen receptor T-cell (CAR-T) therapy, differ from current standard of care treatments. Therefore, limited evidence exists on patient preferences for these novel treatments. This study aimed to identify treatment attributes most important to patients with RRMM to inform a subsequent discrete choice experiment (DCE).

METHODS : This qualitative study used individual, semi-structured interviews with patients with RRMM (n=7) and healthcare professionals (HCP) (n=8) from France and the UK. Myeloma Patients Europe (a patient advocacy organisation) advised on the development of study design to ensure language and questions were framed clearly and understandably for patients. Data were pooled across both countries and analysed separately for patients and HCPs using thematic analysis. HCP interviews aimed to elicit HCP opinions and perceptions of patient preferences only and were not used as a proxy.

RESULTS : Eight overarching themes were identified, and HCPs generally reinforced patient views. Themes described the symptoms and impact on quality of life experienced by patients. Other core themes described the pursuit of effectiveness as the main objective for treatment, a desire to limit side effects, the benefit of reducing centre visits, and a desire for less invasive treatments with fewer doses.

Alongside a relative importance rating exercise, these themes resulted in a shortlist of candidate attributes for an ongoing DCE: progression-free survival, method and location of administration, presence and severity of physical and/or cognitive side effects, treatment duration, dosing schedule and complexity, and distance to administration centre.

CONCLUSIONS : The development of qualitative research materials with a prespecified focus on patient-centric tone and language yields in-depth insights that can subsequently be used to inform further quantitative research, including a DCE.