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Furukawa et al1 posed the question: how can we estimate quality-adjusted life years (QALYs) based on Patient Health Questionnaire-9 (PHQ-9) scores? They recommend equipercentile linking analysis between the depression severity PHQ-9 and preference-based EQ-5D three-level version (EQ-5D-3L; UK value set), the latter used to estimate utility data for QALYs.
Furukawa et al1 refer to the process of ‘cross-walking’, whereby the practice of fitting a statistical model to health utility data has been referred to as ‘mapping’ and 'cross-walking’.2 Furukawa et al1 reference two mapping-related papers (their references 7 and 9); however, their analysis seems to have missed rigorous mapping methodology and previous studies which have used these mapping processes, alongside other conceptual considerations when wanting to ‘cross-walk’/‘map’ from a non-preference-based (often condition-specific) measure such as the PHQ-9 to the preference-based …
Contributors MF and TY provided written contributions throughout the article and act as guarantors for the content of the manuscript.
Funding The writing of the article was part-funded by the National Institute for Health Research (NIHR) Applied Research Collaboration Yorkshire and Humber (NIHR ARC-YH). The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care. The funding agreement ensured the authors’ independence in developing the purview of the manuscript, writing and publishing the manuscript.
Competing interests MF and TY are part funded by the National Institute for Health Research (NIHR) Applied Research Collaboration Yorkshire and Humber (NIHR ARC-YH). As part of the NIHR ARC-YH, we are exploring the potential to map from non-preference-based mental health focussed measures to preference-based measures which includes the Patient Health Questionnaire-9 (PHQ-9) to the EQ-5D three-level version (EQ-5D-3L) or EQ-5D five-level version (EQ-5D-5L).
Provenance and peer review Not commissioned; externally peer reviewed.
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