A community-engaged approach to quantifying caregiver preferences for the benefits and risks of emerging therapies for Duchenne muscular dystrophy.

BACKGROUND There is growing agreement that regulators performing benefit-risk evaluations should take patients' and caregivers' preferences into consideration. The Patient-Focused Drug Development Initiative at the US Food and Drug Administration offers patients and caregivers an enhanced opportunity to contribute to regulatory processes by offering direct testimonials. This process may be advanced by providing scientific evidence regarding treatment preferences through engagement of a broad community of patients and caregivers. OBJECTIVE In this article, we demonstrate a community-engaged approach to measure caregiver preferences for potential benefits and risks of emerging therapies for Duchenne muscular dystrophy (DMD). METHODS An advocacy oversight team led the community-engaged study. Caregivers' treatment preferences were measured by using best-worst scaling (BWS). Six relevant and understandable attributes describing potential benefits and risks of emerging DMD therapies were identified through engagement with advocates (n = 5), clinicians (n = 9), drug developers from pharmaceutical companies and academic centers (n = 11), and other stakeholders (n = 5). The attributes, each defined across 3 levels, included muscle function, life span, knowledge about the drug, nausea, risk of bleeds, and risk of arrhythmia. Cognitive interviewing with caregivers (n = 7) was used to refine terminology and assess acceptability of the BWS instrument. The study was implemented through an online survey of DMD caregivers, who were recruited in the United States through an advocacy group and snowball sampling. Caregivers were presented with 18 treatment profiles, identified via a main-effect orthogonal experimental design, in which the dependent variable was the respondents' judgment as to the best and worst feature in each profile. Preference weights were estimated by calculating the relative number of times a feature was chosen as best and as worst, which were then used to estimate relative attribute importance. RESULTS A total of 119 DMD caregivers completed the BWS instrument; they were predominately biological mothers (67.2%), married (89.9%), and white (91.6%). Treatment effect on muscle function was the most important among experimental attributes (28.7%), followed by risk of heart arrhythmia (22.4%) and risk of bleeding (21.2%). Having additional postapproval data was relatively the least important attribute (2.3%). CONCLUSIONS We present a model process for advocacy organizations aiming to promote patient-centered drug development. The community-engaged approach was successfully used to develop and implement a survey to measure caregiver preferences. Caregivers were willing to accept a serious risk when balanced with a noncurative treatment, even absent improvement in life span. These preferences should inform the Food and Drug Administration's benefit-risk assessment of emerging DMD therapies. This study highlights the synergistic integration of traditional advocacy methods and scientific approach to quantify benefit-risk preferences.

[1]  A. Emery Population frequencies of inherited neuromuscular diseases—A world survey , 1991, Neuromuscular Disorders.

[2]  B. Alman,et al.  Long-term benefits of deflazacort treatment for boys with Duchenne muscular dystrophy in their second decade , 2006, Neuromuscular Disorders.

[3]  P. Zarembka Frontiers in econometrics , 1973 .

[4]  David L. B. Schwappach,et al.  Accounting for Tastes , 2012, PharmacoEconomics.

[5]  P. Hogan,et al.  Cost of illness for neuromuscular diseases in the United States , 2014, Muscle & nerve.

[6]  R. Finkel,et al.  Diagnosis and management of Duchenne muscular dystrophy, part 1: diagnosis, and pharmacological and psychosocial management , 2010, The Lancet Neurology.

[7]  A. Kenneson,et al.  The effect of caregiving on women in families with Duchenne/Becker muscular dystrophy. , 2010, Health & social care in the community.

[8]  Finn Børlum Kristensen,et al.  Exploring Qualitative Research Synthesis , 2011, The patient.

[9]  J. Bridges,et al.  Can Patients Diagnosed with Schizophrenia Complete Choice-Based Conjoint Analysis Tasks? , 2011, The patient.

[10]  T. Peters,et al.  Best--worst scaling: What it can do for health care research and how to do it. , 2007, Journal of health economics.

[11]  D. McFadden Conditional logit analysis of qualitative choice behavior , 1972 .

[12]  M. Fox,et al.  A National Profile of Health Care and Family Impacts of Children With Muscular Dystrophy and Special Health Care Needs in the United States , 2012, Journal of child neurology.

[13]  T. Flynn,et al.  USING BEST-WORST SCALING IN HORIZON SCANNING FOR HEPATOCELLULAR CARCINOMA TECHNOLOGIES , 2012, International Journal of Technology Assessment in Health Care.

[14]  A. Kenneson,et al.  Health Care Utilization and Expenditures for Children and Young Adults With Muscular Dystrophy in a Privately Insured Population , 2008, Journal of child neurology.

[15]  Deborah Marshall,et al.  Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. , 2013, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[16]  John F P Bridges,et al.  Stated preference methods in health care evaluation: an emerging methodological paradigm in health economics. , 2003, Applied health economics and health policy.

[17]  Colin Chandler,et al.  Survival in Duchenne muscular dystrophy: improvements in life expectancy since 1967 and the impact of home nocturnal ventilation , 2002, Neuromuscular Disorders.

[18]  K. Deal Segmenting Patients and Physicians Using Preferences from Discrete Choice Experiments , 2013, The Patient - Patient-Centered Outcomes Research.

[19]  Andrew Lloyd,et al.  Conjoint analysis applications in health--a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. , 2011, Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research.

[20]  John F P Bridges,et al.  Patients' preferences for treatment outcomes for advanced non-small cell lung cancer: a conjoint analysis. , 2012, Lung cancer.

[21]  J. Chamberlain,et al.  Emergent dilated cardiomyopathy caused by targeted repair of dystrophic skeletal muscle. , 2008, Molecular therapy : the journal of the American Society of Gene Therapy.

[22]  B S Levitan,et al.  Development of a Framework for Enhancing the Transparency, Reproducibility and Communication of the Benefit–Risk Balance of Medicines , 2011, Clinical pharmacology and therapeutics.

[23]  Rachael Fleurence,et al.  How the Patient-Centered Outcomes Research Institute is engaging patients and others in shaping its research agenda. , 2013, Health affairs.

[24]  A. Brett Hauber,et al.  Quantifying Benefit–Risk Preferences for Medical Interventions: An Overview of a Growing Empirical Literature , 2013, Applied Health Economics and Health Policy.

[25]  S. Kripalani,et al.  Validation of a Short, 3-Item Version of the Subjective Numeracy Scale , 2015, Medical decision making : an international journal of the Society for Medical Decision Making.

[26]  L. Prosser,et al.  A CHECKLIST FOR CONJOINT ANALYSIS APPLICATIONS IN HEALTH : REPORT OF THE ISPOR CONJOINT ANALYSIS GOOD RESEARCH PRACTICES TASK FORCE FINAL DRAFT : MAY 16 , 2008 For comment only , 2009 .

[27]  N. Bresolin,et al.  Ongoing therapeutic trials and outcome measures for Duchenne muscular dystrophy , 2013, Cellular and Molecular Life Sciences.

[28]  T. Coté,et al.  Duchenne muscular dystrophy: Drug development and regulatory considerations , 2010, Muscle & nerve.

[29]  T. Flynn Valuing citizen and patient preferences in health: recent developments in three types of best–worst scaling , 2010, Expert review of pharmacoeconomics & outcomes research.

[30]  K. Facey,et al.  Patients' perspectives in health technology assessment: A route to robust evidence and fair deliberation , 2010, International Journal of Technology Assessment in Health Care.

[31]  F. Johnson,et al.  A brief introduction to the use of stated-choice methods to measure preferences for treatment benefits and risks , 2009 .

[32]  Joanna Coast,et al.  Maximising Responses to Discrete Choice Experiments , 2006, Applied health economics and health policy.

[33]  E. John Orav,et al.  Triage decisions for emergency department patients with chest pain , 1995, Journal of General Internal Medicine.

[34]  P. Slovic,et al.  Numeracy skill and the communication, comprehension, and use of risk-benefit information. , 2007, Health affairs.

[35]  E. Niggli,et al.  Cardiac phenotype of Duchenne Muscular Dystrophy: insights from cellular studies. , 2013, Journal of molecular and cellular cardiology.

[36]  B. Orme Getting Started with Conjoint Analysis: Strategies for Product Design and Pricing Research , 2005 .

[37]  P. Ubel,et al.  Validation of the Subjective Numeracy Scale: Effects of Low Numeracy on Comprehension of Risk Communications and Utility Elicitations , 2007, Medical decision making : an international journal of the Society for Medical Decision Making.

[38]  C. Angelini The role of corticosteroids in muscular dystrophy: A critical appraisal , 2007, Muscle & nerve.

[39]  S. Pandya,et al.  Prevalence of Duchenne/Becker muscular dystrophy among males aged 5-24 years - four states, 2007. , 2009, MMWR. Morbidity and mortality weekly report.

[40]  P. Hogan,et al.  Cost of illness for neuromuscular diseases in the U.S , 2014 .

[41]  Dan Rigby,et al.  Using best–worst scaling to explore perceptions of relative responsibility for ensuring food safety , 2012 .

[42]  Patient-focused drug development programme takes first steps , 2013, Nature Reviews Drug Discovery.

[43]  Deborah Marshall,et al.  Conjoint Analysis Applications in Health — How are Studies being Designed and Reported? , 2010, The patient.

[44]  C. Milne Prospects for Rapid Advances in the Development of New Medicines for Special Medical Needs , 2013, Clinical pharmacology and therapeutics.

[45]  T. Öberg,et al.  Conjoint analysis , 2008, Environmental science and pollution research international.

[46]  P. Ubel,et al.  Measuring Numeracy without a Math Test: Development of the Subjective Numeracy Scale , 2007, Medical decision making : an international journal of the Society for Medical Decision Making.

[47]  H. Stam,et al.  Subjective caregiver burden of parents of adults with Duchenne muscular dystrophy , 2012, Disability and rehabilitation.

[48]  Elizabeth Greenberg,et al.  A First Look at the Literacy of America's Adults in the 21st Century. NCES 2006-470. , 2006 .

[49]  R. Griggs,et al.  Report on the 124th ENMC International Workshop. Treatment of Duchenne muscular dystrophy; defining the gold standards of management in the use of corticosteroids 2–4 April 2004, Naarden, The Netherlands , 2004, Neuromuscular Disorders.

[50]  Terry N Flynn,et al.  Using Best-Worst Scaling Choice Experiments to Measure Public Perceptions and Preferences for Healthcare Reform in Australia , 2010, The patient.

[51]  Richard Emsley,et al.  Applying Best-Worst scaling methodology to establish delivery preferences of a symptom supportive care intervention in patients with lung cancer. , 2012, Lung cancer.