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      Patient preferences and priorities for haemophilia gene therapy in the US: A discrete choice experiment

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          Abstract

          Introduction

          Gene therapy has shown promise in clinical trials for patients with haemophilia, but patient preference studies have focused on factor replacement treatments.

          Aim

          We conducted a discrete choice experiment (DCE) to investigate the relative importance and differential preferences patients provide for gene therapy attributes.

          Methods

          We surveyed male adults with haemophilia in the United States recruited from patient panels including the National Hemophilia Foundation Community Voices in Research platform using an online survey over 4 months in 2020/21. Participants indicated preferences for gene therapy attributes including dosing frequency/durability, effect on annual bleeding, uncertainty related to side effects, impact on daily activities, impact on mental health, and post‐treatment requirements. The relative importance of each attribute was analysed overall and for subgroups based on haemophilia type and severity.

          Results

          A total of 183 males with haemophilia A (n = 120) or B (n = 63) were included. Half (47%) had severe haemophilia; most (75%) were White. Overall, participants gave effect on bleeding rate the greatest relative importance (31%), followed by dose frequency/durability (26%), uncertainty regarding safety issues (17%), and impact on daily activities (11%). Dose frequency/durability had the greatest importance for those with haemophilia B (35%).

          Conclusion

          People with haemophilia prioritised reduced bleeding and treatment burden; the former was more important in haemophilia A and the latter in haemophilia B, followed by safety and impact on daily life in this DCE of gene therapy attributes. These findings and differences can inform clinical and health policy decisions to improve health equity for people with haemophilia.

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          Most cited references24

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          Statistical Methods for the Analysis of Discrete Choice Experiments: A Report of the ISPOR Conjoint Analysis Good Research Practices Task Force.

          Conjoint analysis is a stated-preference survey method that can be used to elicit responses that reveal preferences, priorities, and the relative importance of individual features associated with health care interventions or services. Conjoint analysis methods, particularly discrete choice experiments (DCEs), have been increasingly used to quantify preferences of patients, caregivers, physicians, and other stakeholders. Recent consensus-based guidance on good research practices, including two recent task force reports from the International Society for Pharmacoeconomics and Outcomes Research, has aided in improving the quality of conjoint analyses and DCEs in outcomes research. Nevertheless, uncertainty regarding good research practices for the statistical analysis of data from DCEs persists. There are multiple methods for analyzing DCE data. Understanding the characteristics and appropriate use of different analysis methods is critical to conducting a well-designed DCE study. This report will assist researchers in evaluating and selecting among alternative approaches to conducting statistical analysis of DCE data. We first present a simplistic DCE example and a simple method for using the resulting data. We then present a pedagogical example of a DCE and one of the most common approaches to analyzing data from such a question format-conditional logit. We then describe some common alternative methods for analyzing these data and the strengths and weaknesses of each alternative. We present the ESTIMATE checklist, which includes a list of questions to consider when justifying the choice of analysis method, describing the analysis, and interpreting the results.
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            The challenge of patient adherence

            Quality healthcare outcomes depend upon patients' adherence to recommended treatment regimens. Patient nonadherence can be a pervasive threat to health and wellbeing and carry an appreciable economic burden as well. In some disease conditions, more than 40% of patients sustain significant risks by misunderstanding, forgetting, or ignoring healthcare advice. While no single intervention strategy can improve the adherence of all patients, decades of research studies agree that successful attempts to improve patient adherence depend upon a set of key factors. These include realistic assessment of patients' knowledge and understanding of the regimen, clear and effective communication between health professionals and their patients, and the nurturance of trust in the therapeutic relationship. Patients must be given the opportunity to tell the story of their unique illness experiences. Knowing the patient as a person allows the health professional to understand elements that are crucial to the patient's adherence: beliefs, attitudes, subjective norms, cultural context, social supports, and emotional health challenges, particularly depression. Physician–patient partnerships are essential when choosing amongst various therapeutic options to maximize adherence. Mutual collaboration fosters greater patient satisfaction, reduces the risks of nonadherence, and improves patients' healthcare outcomes.
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              Discrete choice experiments in health care.

              Mandy Ryan (2004)

                Author and article information

                Contributors
                george.morgan@hcdeconomics.com
                Journal
                Haemophilia
                Haemophilia
                10.1111/(ISSN)1365-2516
                HAE
                Haemophilia
                John Wiley and Sons Inc. (Hoboken )
                1351-8216
                1365-2516
                26 July 2021
                September 2021
                : 27
                : 5 ( doiID: 10.1111/hae.v27.5 )
                : 769-782
                Affiliations
                [ 1 ] National Hemophilia Foundation New York USA
                [ 2 ] HCD Economics Daresbury UK
                [ 3 ] Faculty of Health and Social Care University of Chester Chester UK
                [ 4 ] Oregon Health & Science University Portland Oregon USA
                [ 5 ] American Thrombosis & Hemostasis Network Rochester New York USA
                [ 6 ] Hemophilia and Thrombosis Center University of Colorado School of Medicine Aurora Colorado USA
                [ 7 ] Department of Pediatrics and Division of Hematology at CHOC Children's Hospital Center for Inherited Blood Disorders Orange California USA
                [ 8 ] Factor VIII Computing Berkeley California USA
                [ 9 ] Irish Haemophilia Society Dublin UK
                [ 10 ] Trinity College Dublin UK
                [ 11 ] Institute for Policy Advancement, Ltd. Washington District of Columbia USA
                [ 12 ] McMaster University Hamilton Ontario Canada
                [ 13 ] Centre for Health Economics Research and Evaluation University of Technology Sydney Australia
                [ 14 ] uniQure Inc Lexington Massachusetts USA
                Author notes
                [*] [* ] Correspondence

                George Morgan, HCD Economics, Daresbury, WA4 4FS, UK.

                Email: george.morgan@ 123456hcdeconomics.com

                Author information
                https://orcid.org/0000-0003-2014-3415
                https://orcid.org/0000-0002-6859-6432
                https://orcid.org/0000-0001-9780-6972
                https://orcid.org/0000-0002-0934-0680
                Article
                HAE14383
                10.1111/hae.14383
                9290457
                34310811
                c473efdc-477d-44ff-a242-536eb6ff8ab1
                © 2021 The Authors. Haemophilia published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc-nd/4.0/ License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made.

                History
                : 23 June 2021
                : 29 April 2021
                : 15 July 2021
                Page count
                Figures: 4, Tables: 7, Pages: 14, Words: 6776
                Categories
                Original Article
                Original Articles
                Clinical Haemophilia
                Custom metadata
                2.0
                September 2021
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.1.7 mode:remove_FC converted:18.07.2022

                Hematology
                discrete choice experiment,haemophilia,health equity,patient preference
                Hematology
                discrete choice experiment, haemophilia, health equity, patient preference

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