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Aging Clinical and Experimental Research

, Volume 31, Issue 1, pp 1–3 | Cite as

Patients’ preferences for osteoarthritis treatment: the value of stated-preference studies

  • Mickael HiligsmannEmail author
  • Daniel Pinto
  • Elaine Dennison
  • Nasser Al-Daghri
  • Charlotte Beaudart
  • Jaime Branco
  • Olivier Bruyère
  • Philip G. Conaghan
  • Cyrus Cooper
  • Gabriel Herrero-Beaumont
  • Famida Jiwa
  • Willem Lems
  • Rene Rizzoli
  • Thierry Thomas
  • Nicola Veronese
  • Jean-Yves Reginster
Editorial

In recent years, there has been a growing interest in obtaining patients’ preferences for healthcare treatments that are deemed ‘preference sensitive’. In particular, the use of stated-preference studies (including discrete-choice experiments (DCE), conjoint analysis, and best–worst scaling) has markedly increased [1]. Preference-sensitive treatments are those in which there are trade-offs between health benefits and risks and the patient’s consideration of these factors is of utmost importance in the eventual utilization of such services.

Stated-preference studies originated in marketing as a means of better understanding the wants and needs of the consumer. A main assumption of DCE is that a treatment can be broken down into its attributes (such as effectiveness, side effects and mode of administration) and that the utility a person receives from the treatment is a combination of these attributes. In such studies, respondents (generally patients) are asked to repeatedly choose between two or more hypothetical treatments that differ according to attributes of interest. DCEs can quantify the relative importance of the various attributes that characterize a treatment by quantifying the trade-offs that respondents make from their choices.

Given the significant challenges and lack of therapeutic options for osteoarthritis (OA), it is not surprising that several stated-preference studies have been conducted to elicit preferences for OA treatment. Preference-sensitive decisions are mainly adapted for non-urgent, non-fatal diseases and, therefore, particularly adapted to OA. OA is the most common form of arthritis and most frequently affects the knee, hand, and/or hip. OA is predominantly characterized by pain and has been shown to substantially reduce the patient’s mobility and quality of life and to represent a significant contributor to disability in the elderly. Currently, OA treatments aim primarily to reduce joint pain, maintain and improve joint mobility and enhance quality of life. Treatment options (including surgery, pharmacological and non-pharmacological treatment) may, however, differ in benefits and risks, emphasizing the need to assess patients’ preferences for the different aspects of OA treatment.

In the OA field, stated-preference studies have primarily been conducted to assess the preferences for the characteristics of OA drug treatment. Most of these studies were DCEs, reflecting that the structure of DCEs seems appropriate to the target decision-making situation. Potential benefits and risk of adverse events have to date been shown to be the most influential characteristics for both patients and physicians. In some studies [2, 3, 4], benefit attributes (such as improvement in function or reduction in pain) were the most important attributes while other studies reported that patients were more concerned by the risk of side effects [5, 6, 7]. Costs and mode of administration have also been shown to be significant predictors of preferences in some studies.

Several other applications of stated-preference study have been observed in the field of OA. The study of Coxon et al. [8] investigated the decision to consult a primary care physician for painful OA and assessed the relative importance of perceived service-related and clinical need attributes in this decision. Another DCE [9] assessed the relative importance of attributes for physical activity treatment to improve knee osteoarthritis. DCEs have also been conducted to reveal patients’ preferences for surgical treatment for knee OA [10, 11]. Studies were also identified that reported on patients’ experiences [12, 13] for the use of attribute-based preference methods as a decision aid in total knee arthroplasty and a protocol study for a randomized controlled trial has been published [14]. It should be noted that alternatives beside stated-preference studies exist for assessing patient preferences for an aspect of their care, such as the McMaster-Toronto Arthritis Patient Preference Disability Questionnaire. The MACTAR questionnaire assesses priorities in disability and restriction in participation among patients with knee OA [15]. Unlike stated-choice experiments, patient-reported outcomes do not have the same theoretical origin and do not collapse a respondent’s profile into a single utility number representing overall value. Although useful, patient-reported outcomes capture patient reports of outcomes in individual domains and thus do not provide information about patients’ preferences across domains [16].

The patient’s perspective is becoming increasingly important in clinical and policy decisions. Information about what patients need and prefer, and how they value various aspects of a health intervention can be useful when designing and evaluating healthcare programs [17]. Such insights can further help when establishing treatment guidelines and should be taken into consideration when developing new drugs or other interventions. Furthermore, regulatory agencies such as the European Medicines Agency and the US Food and Drug Administration are already evaluating quantitative approaches to inform benefit/risk assessment, and preferences could be important when making decisions about the reimbursement of new therapies. A better understanding of patients’ preferences for treatment can also help health professionals to improve disease management. Addressing patients’ concerns with treatment and involving them in clinical decision-making may improve treatment adherence [18]. Patients increasingly want to be kept informed by their doctors, and to be active in clinical decision-making. Therefore, the next step is to include their thoughts and preferences in the design, objectives and assessed parameters of clinical research studies.

In conclusion, the use of stated-preference studies has been shown to be feasible in OA and provides relevant information regarding preferences for OA treatment. Previous studies have suggested that OA patients are most concerned about efficacy and risks of OA treatment. Insights into the preferences of patients will be useful to optimize policy and clinical decision-making through healthcare decision-making that better reflects patients’ preferences.

Notes

Acknowledgements

No funding was received for this study. The authors are grateful to the Prince Mutaib Chair for Biomarkers of Osteoporosis, King Saud University, Riyadh, Saudi Arabia, for its support.

Compliance with ethical standards

Conflict of interest

OB: research supports from Biophytis, IBSA, MEDA, Servier, SMB. PGC is supported in part by the UK NIHR Leeds Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. CC: lecture fees and honoraria from Amgen, Danone, Eli Lilly, GSK, Medtronic, Merck, Nestlé, Novartis, Pfizer, Roche, Servier, Shire, Takeda and UCB outside of the submitted work. TT: honorarium for lectures or advisory board from Abbvie, Amgen, Arrow, BMS, Chugai, Expanscience, Gilead, HAC-Pharma, LCA, Lilly, Medac, MSD, Pfizer, Thuasne, TEVA and UCB; research support from Amgen, Bone Therapeutics, Chugai, HAC-Pharma, MSD, Novartis, Pfizer, and UCB. JYR: consulting fees or paid advisory boards from IBSA-Genevrier, Mylan, Radius Health, Pierre Fabre, Teva; lecture fees when speaking at the invitation of sponsor: IBSA-Genevrier, Mylan, CNIEL, Dairy Research Council, Teva; Grant support from industry (all through institution) from IBSA-Genevrier, Mylan, CNIEL, Radius Health. OB research supports from Biophytis, IBSA, MEDA, Servier, SMB. NAD, CB, JB, CC, MH, GHB, FJ, DP, WL, and RR have no conflict of interest relevant to the content of this study.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mickael Hiligsmann
    • 1
    Email author
  • Daniel Pinto
    • 2
  • Elaine Dennison
    • 3
  • Nasser Al-Daghri
    • 4
  • Charlotte Beaudart
    • 5
  • Jaime Branco
    • 6
  • Olivier Bruyère
    • 5
  • Philip G. Conaghan
    • 7
  • Cyrus Cooper
    • 3
    • 8
  • Gabriel Herrero-Beaumont
    • 9
  • Famida Jiwa
    • 10
  • Willem Lems
    • 11
  • Rene Rizzoli
    • 12
  • Thierry Thomas
    • 13
  • Nicola Veronese
    • 14
  • Jean-Yves Reginster
    • 4
    • 5
  1. 1.Department of Health Services Research, CAPHRI Care and Public Health Research InstituteMaastricht UniversityMaastrichtThe Netherlands
  2. 2.Department of Physical TherapyMarquette UniversityMilwaukeeUSA
  3. 3.MRC Lifecourse Epidemiology UnitUniversity of SouthamptonSouthamptonUK
  4. 4.Prince Mutaib Chair for Biomarkers of Osteoporosis, Biochemistry Department, College of ScienceKing Saud UniversityRiyadhKingdom of Saudi Arabia
  5. 5.Department of Public Health, Epidemiology and Health EconomicsUniversity of LiègeLiègeBelgium
  6. 6.Rheumatology DepartmentCEDOC-NOVA Medical School, Faculdade de Ciências Médicas da Universidade Nova de Lisboa, CHLO, Hospital Egas MonizLisbonPortugal
  7. 7.Leeds Institute of Rheumatic and Musculoskeletal Medicine, University of Leeds, and NIHR Leeds Biomedical Research CentreLeedsUK
  8. 8.NIHR Musculoskeletal Biomedical Research UnitUniversity of OxfordOxfordUK
  9. 9.Head of Rheumatology DepartmentIIS-Fundacion Jimenez Diaz, UAMMadridSpain
  10. 10.Chair of the Committee of Patients Societies at the International Osteoporosis Foundation, Osteoporosis CanadaTorontoCanada
  11. 11.VU University Medical CenterAmsterdamThe Netherlands
  12. 12.Geneva University Hospitals and Faculty of MedicineGenevaSwitzerland
  13. 13.Department of RheumatologyNord Hospital, University Hospital of St-Etienne, INSERM 1059, University of LyonSaint-ÉtienneFrance
  14. 14.Neuroscience Institute, Aging BranchNational Research CouncilPaduaItaly

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