TY - JOUR
T1 - Obtaining EQ-5D-5L utilities from the disease specific quality of life Alzheimer’s disease scale
T2 - development and results from a mapping study
AU - Rombach, Ines
AU - Iftikhar, Marvi
AU - Jhuti, Gurleen S.
AU - Gustavsson, Anders
AU - Lecomte, Pascal
AU - Belger, Mark
AU - Handels, Ron
AU - Castro Sanchez, Amparo Y.
AU - Kors, Jan
AU - Hopper, Louise
AU - Olde Rikkert, Marcel
AU - Selbæk, Geir
AU - Stephan, Astrid
AU - Sikkes, Sietske A.M.
AU - Woods, Bob
AU - Gonçalves-Pereira, Manuel
AU - Zanetti, Orazio
AU - Ramakers, Inez H.G.B.
AU - Verhey, Frans R.J.
AU - Gallacher, John
AU - Actifcare Consortium, Consortium
AU - LeARN Consortium, Consortium
AU - Landeiro, Filipa
AU - on behalf of ROADMAP Consortium,
AU - Gray, Alastair M.
N1 - Funding Information:
This project has received funding from the Innovative Medicines Initiative 2 Joint Undertaking under grant agreement No. 116020 (“ROADMAP (Real world Outcomes across the AD spectrum for better care: Multi-modal data Access Platform)”). This Joint Undertaking receives support from the European Union’s Horizon 2020 research and innovation program and EFPIA. It is also supported by the Medical Research Council Dementias Platform UK (MR/L023784/1 and MR/009076/1). AMG was partly supported by the National Institute for Health Research (NIHR) Oxford Biomedical Research Centre (BRC). The views expressed are those of the author and not necessarily those of the NHS, the NIHR or the Department of Health. This project was conducted in collaboration with Dementias Platform UK and the European Medical Informatics Framework. The Actifcare project is an EU Joint Programme—Neurodegenerative Disease Research (JPND) project. The project is supported through the following funding organisations under the aegis of JPND— www.jpnd.eu (Germany, Bundesministerium für Bildung und Forschung (BMBF), Ireland, Health Research Board (HRB), Italy, Italian Ministry of Health, Netherlands, The Netherlands Organization for Health Research and Development (ZonMW)/Alzheimer Netherlands, Norway, The Research Council of Norway, Portugal, Fundação para a Ciência e a Tecnologia (FCT-JPND-HC/0001/2012), Sweden, Swedish Research Council (SRC), United Kingdom, Economic and Social Research Council (ESRC)). The Actifcare Consortium partners are: Coordinator: Maastricht University (NL): Frans Verhey, professor (scientific coordinator, WP1 leader) Consortium members: Maastricht University (NL): Marjolein de Vugt, Claire Wolfs, Ron Handels, Liselot Kerpershoek. Martin-Luther University Halle-Wittenberg (DE): Gabriele Meyer (WP2 leader), Astrid Stephan, Anja Bieber. Bangor University (UK): Bob Woods (WP3 leader), Hannah Jelley Nottingham University (UK): Martin Orrell, Karolinska Institutet (SE): Anders Wimo (WP4 leader), Anders Sköldunger, Britt-Marie Sjölund, Oslo University Hospital (NW): Knut Engedal, Geir Selbaek (WP5 leader), Mona Michelet, Janne Rosvik, Siren Eriksen. Dublin City University (IE): Kate Irving (WP6 leader), Louise Hopper, Rachael Joyce. CEDOC, Nova Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa (PT): Manuel Gonçalves-Pereira, Maria J. Marques, M. Conceição Balsinha, Ana Machado, on behalf of the Portuguese Actifcare team. IRCCS Istituto Centro S. Giovanni di Dio Fatebenefratelli, Brescia (IT): Orazio Zanetti, Daniel Michael Portolani. We thank the LeARN Consortium for data acquisition. The LeARN project was performed within the framework of CTMM, The Center for Translational Molecular Medicine ( www.ctmm.nl ) project LeARN (Grant 02N‐101).
Funding Information:
The authors declare the following conflict of interest: Gurleen S Jhuti is an employee of F. Hoffmann-La Roche Ltd. Anders Gustavsson is a partner of Quantify Research, providing consultancy services to pharmaceutical companies and other private and public organisations and institutions. Anders Gustavson’s contribution to ROADMAP was on behalf of Roche Pharmaceuticals. Pascal Lecomte is a full-time employee of Novartis and holds stocks of Novartis. Mark Belger is an employee and shareholder of Eli Lilly and Company. Ron Handels reports the following to conduct this study: grants from ROADMAP (IMI2; public-private collaboration; 2016–2019); Ron Handels reports the following outside this study: consulting fees from Piramal, Roche and Eisai; grants from Horizon 2020, JPND Joint Programming Neurodegenerative Disease Research, IMI Innovative Medicines Initiative, and national, European and patient charity funding organizations and private-public collaborations (ZonMw Netherlands; Alzheimer Netherlands; Dutch Flutemetamol Study; Alzheimer Research UK; Swedish National study on Aging and Care; European Brain Council). Yovanna Castro: Yovanna Castro is an employee of F. Hoffmann-La Roche Ltd. F. Hoffmann-La Roche Ltd. is an industry partner in the ROADMAP project. Ines Rombach, Filipa Landeiro and Alastair M Gray report grants from Innovative Medicines Initiative 2 Joint Undertaking during the conduct of the study. The remaining authors have no conflict of interest to declare.
Publisher Copyright:
© 2020, The Author(s).
Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.
PY - 2020/10/17
Y1 - 2020/10/17
N2 - Purpose: The Quality of Life Alzheimer’s Disease Scale (QoL-AD) is commonly used to assess disease specific health-related quality of life (HRQoL) as rated by patients and their carers. For cost-effectiveness analyses, utilities based on the EQ-5D are often required. We report a new mapping algorithm to obtain EQ-5D indices when only QoL-AD data are available. Methods: Different statistical models to estimate utility directly, or responses to individual EQ-5D questions (response mapping) from QoL-AD, were trialled for patient-rated and proxy-rated questionnaires. Model performance was assessed by root mean square error and mean absolute error. Results: The response model using multinomial regression including age and sex, performed best in both the estimation dataset and an independent dataset. Conclusions: The recommended mapping algorithm allows researchers for the first time to estimate EQ-5D values from QoL-AD data, enabling cost-utility analyses using datasets where the QoL-AD but no utility measures were collected.
AB - Purpose: The Quality of Life Alzheimer’s Disease Scale (QoL-AD) is commonly used to assess disease specific health-related quality of life (HRQoL) as rated by patients and their carers. For cost-effectiveness analyses, utilities based on the EQ-5D are often required. We report a new mapping algorithm to obtain EQ-5D indices when only QoL-AD data are available. Methods: Different statistical models to estimate utility directly, or responses to individual EQ-5D questions (response mapping) from QoL-AD, were trialled for patient-rated and proxy-rated questionnaires. Model performance was assessed by root mean square error and mean absolute error. Results: The response model using multinomial regression including age and sex, performed best in both the estimation dataset and an independent dataset. Conclusions: The recommended mapping algorithm allows researchers for the first time to estimate EQ-5D values from QoL-AD data, enabling cost-utility analyses using datasets where the QoL-AD but no utility measures were collected.
KW - Cross-walking
KW - Dementia
KW - Health related quality of life
KW - Mapping algorithm
KW - Preference based measures
UR - http://www.scopus.com/inward/record.url?scp=85092760168&partnerID=8YFLogxK
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U2 - 10.1007/s11136-020-02670-8
DO - 10.1007/s11136-020-02670-8
M3 - Article
AN - SCOPUS:85092760168
SN - 0962-9343
SP - Epub Ahead of Print
JO - Quality of Life Research
JF - Quality of Life Research
ER -