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Engaging patients and caregivers in prioritizing symptoms impacting quality of life for Duchenne and Becker muscular dystrophy

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Abstract

Purpose

Patient preference information (PPI) have an increasing role in regulatory decision-making, especially in benefit–risk assessment. PPI can also facilitate prioritization of symptoms to treat and inform meaningful selection of clinical trial endpoints. We engaged patients and caregivers to prioritize symptoms of Duchenne and Becker muscular dystrophy (DBMD) and explored preference heterogeneity.

Methods

Best–worst scaling (object case) was used to assess priorities across 11 symptoms of DBMD that impact quality of life and for which there is unmet need. Respondents selected the most and least important symptoms to treat among a subset of five. Relative importance scores were estimated for each symptom, and preference heterogeneity was identified using mixed logit and latent class analysis.

Results

Respondents included patients (n = 59) and caregivers (n = 96) affected by DBMD. Results indicated that respondents prioritized “weaker heart pumping” [score = 5.13; 95% CI (4.67, 5.59)] and pulmonary symptoms: “lung infections” [3.15; (2.80, 3.50)] and “weaker ability to cough” [2.65; (2.33, 2.97)] as the most important symptoms to treat and “poor attention span” as the least important symptom to treat [− 5.23; (− 5.93, − 4.54)]. Statistically significant preference heterogeneity existed (p value < 0.001). At least two classes existed with different priorities. Priorities of the majority latent class (80%) reflected the aggregate results, whereas the minority latent class (20%) did not distinguish among pulmonary and other symptoms.

Conclusions

Estimates of the relative importance for symptoms of Duchenne muscular dystrophy indicated that symptoms with direct links to morbidity and mortality were prioritized above other non-skeletal muscle symptoms. Findings suggested the existence of preference heterogeneity for symptoms, which may be related to symptom experience.

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Change history

  • 18 November 2022

    ORCID of first author is updated.

References

  1. Emery, A. E. (2002). The muscular dystrophies. The Lancet, 359(9307), 687–695.

    CAS  Google Scholar 

  2. Emery, A. E. (1991). Population frequencies of inherited neuromuscular diseases: A world survey. Neuromuscular Disorders, 1(1), 19–29.

    CAS  PubMed  Google Scholar 

  3. Mah, J. K., Korngut, L., Dykeman, J., Day, L., Pringsheim, T., & Jette, N. (2014). A systematic review and meta-analysis on the epidemiology of Duchenne and Becker muscular dystrophy. Neuromuscular Disorders, 24(6), 482–491.

    PubMed  Google Scholar 

  4. Bushby, K., Finkel, R., Birnkrant, D. J., Case, L. E., Clemens, P. R., Cripe, L., et al. (2010). Diagnosis and management of Duchenne muscular dystrophy, part 1: Diagnosis, and pharmacological and psychosocial management. The Lancet. Neurology, 9(1), 77–93.

    PubMed  Google Scholar 

  5. Cyrulnik, S. E., Fee, R. J., Batchelder, A., Kiefel, J., Goldstein, E., & Hinton, V. J. (2008). Cognitive and adaptive deficits in young children with Duchenne muscular dystrophy (DMD). Journal of the International Neuropsychological Society, 14(5), 853–861.

    PubMed  Google Scholar 

  6. Guglieri, M., Bushby, K., McDermott, M. P., Hart, K. A., Tawil, R., & Martens, W. B. (2017). Developing standardized corticosteroid treatment for Duchenne muscular dystrophy. Contemporary Clinical Trials, 58, 34–39.

    PubMed  PubMed Central  Google Scholar 

  7. Traynor, K. (2017). Deflazacort approved for Duchenne muscular dystrophy. American Journal of Health-System Pharmacy, 74(6), 368.

    PubMed  Google Scholar 

  8. Aartsma-Rus, A., & Krieg, A. M. (2017). FDA approves Eteplirsen for Duchenne muscular dystrophy: The next chapter in the Eteplirsen saga. Nucleic Acid Therapeutics, 27(1), 1–3.

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Unger, E. F., & Califf, R. M. (2017). Regarding “Eteplirsen for the treatment of Duchenne muscular dystrophy”. Annals of Neurology, 81(1), 162–164.

    PubMed  Google Scholar 

  10. Niks, E. H., & Aartsma-Rus, A. (2017). Exon skipping: A first in class strategy for Duchenne muscular dystrophy. Expert Opinion on Biological Therapy, 17(2), 225–236.

    CAS  PubMed  Google Scholar 

  11. Medical Device Innovation Consortium (MDIC). (2015). Patient centered benefit-risk project report: A framework for incorporating information on patient preferences regarding benefit and risk into regulatory assessments of new medical technology. Arlington: Medical Device Innovation Consortium (MDIC). Retrieved November 3, 2017, from http://mdic.org/wp-content/uploads/2015/05/MDIC_PCBR_Framework_Proof5_Web.pdf.

  12. US Food and Drug Administration. (2016) Patient preference information—voluntary submission, review in premarket approval applications, humanitarian device exemption applications, and de novo requests, and inclusion in decision summaries and device labeling: Guidance for industry, food and drug administration staff, and other stakeholders. Silver Spring: Center for Devices and Radiological Health, Center for Biologics Evaluation and Research, Food and Drug Administration. Retrieved November 3, 2017, from https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm446680.pdf.

  13. Woodward, A. T. (2013). A latent class analysis of age differences in choosing service providers to treat mental and substance use disorders. Psychiatric Services, 64(11), 1087–1094.

    PubMed  Google Scholar 

  14. Wong, Y. N., Egleston, B. L., Sachdeva, K., Eghan, N., Pirollo, M., Stump, T. K., et al. (2013). Cancer patients’ trade-offs among efficacy, toxicity and out-of-pocket cost in the curative and noncurative setting. Medical Care, 51(9), 838–845.

    PubMed  Google Scholar 

  15. Whitty, J. A., Stewart, S., Carrington, M. J., Calderone, A., Marwick, T., Horowitz, J. D., et al. (2013). Patient preferences and willingness-to-pay for a home or clinic based program of chronic heart failure management: Findings from the which? trial. PLoS ONE, 8(3), e58347.

    CAS  PubMed  PubMed Central  Google Scholar 

  16. Waschbusch, D. A., Cunningham, C. E., Pelham, W. E. Jr., Rimas, H. L., Greiner, A. R., Gnagy, E. M., et al. (2011). A discrete choice conjoint experiment to evaluate parent preferences for treatment of young, medication naive children with ADHD. Journal of Clinical Child and Adolescent Psychology, 40(4), 546–561.

    PubMed  PubMed Central  Google Scholar 

  17. Naik-Panvelkar, M. P., Armour, C., Rose, J.,M., & Saini, B. (2012). Patient preferences for community pharmacy asthma services. PharmacoEconomics, 30(10), 961–976.

    PubMed  Google Scholar 

  18. Lagarde, M. (2013). Investigating attribute non-attendance and its consequences in choice experiments with latent class models. Health Economics, 22(5), 554–567.

    PubMed  Google Scholar 

  19. Guo, N., Marra, C. A., FitzGerald, J. M., Elwood, R. K., Anis, A. H., & Marra, F. (2011). Patient preference for latent tuberculosis infection preventive treatment: A discrete choice experiment. Value in Health, 14(6), 937–943.

    PubMed  Google Scholar 

  20. Goossens, L. M., Utens, C. M., Smeenk, F. W., Donkers, B., van Schayck, O. C., & Rutten-van Mölken, M. P. (2014). Should I stay or should I go home? A latent class analysis of a discrete choice experiment on hospital-at-home. Value in Health, 17(5), 588–596.

    PubMed  Google Scholar 

  21. Fraenkel, L., Suter, L., Cunningham, C. E., & Hawker, G. (2014). Understanding preferences for disease-modifying drugs in osteoarthritis. Arthritis Care and Research, 66(8), 1186–1192.

    PubMed  Google Scholar 

  22. Cunningham, C. E., Chen, Y., Deal, K., Rimas, H., McGrath, P., Reid, G., et al. (2013). The interim service preferences of parents waiting for children’s mental health treatment: A discrete choice conjoint experiment. Journal of Abnormal Child Psychology, 41(6), 865–877.

    PubMed  Google Scholar 

  23. Carroll, F. E., Al-Janabi, H., Flynn, T., & Montgomery, A. A. (2013). Women and their partners’ preferences for Down’s syndrome screening tests: A discrete choice experiment. Prenatal Diagnosis, 33(5), 449–456.

    PubMed  Google Scholar 

  24. Brown, D. S., Poulos, C., Johnson, F. R., Chamiec-Case, L., & Messonnier, M. L. (2014). Adolescent girls’ preferences for HPV vaccines: A discrete choice experiment. Advances in Health Economics and Health Services Research, 24, 93–121.

    PubMed  Google Scholar 

  25. Yan, K., Bridges, J. F., Augustin, S., Laine, L., Garcia-Tsao, G., & Fraenkel, L. (2015). Factors impacting physicians decisions to prevent variceal hemorrhage. BMC Gastroenterology, 15, 55.

    PubMed  PubMed Central  Google Scholar 

  26. Fraenkel, L., Lim, J., Garcia-Tsao, G., Reyna, V., Monto, A., & Bridges, J. F. P. (2016). Variation in treatment priorities for chronic Hepatitis C: A latent class analysis. The Patient, 9(3), 241–249.

    PubMed  Google Scholar 

  27. US Food and Drug Administration. (2012) Guidance for industry and food and drug administration staff: Factors to consider when making benefit-risk determinations in medical device premarket approval and de novo classifications. Silver Spring: Center for Devices and Radiological Health, Center for Biologics Evaluation and Research, Food and Drug Administration. Retrieved November 3, 2017, from https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm517504.pdf.

  28. Hunter, N. L., O’Callaghan, K. M., & Califf, R. M. (2015). Engaging patients across the spectrum of medical product development: View from the US Food and Drug Administration. Journal of the American Medical Association, 314(23), 2499–2500.

    CAS  PubMed  Google Scholar 

  29. Ho, M. P., Gonzalez, J. M., Lerner, H. P., Neuland, C. Y., Whang, J. M., McMurry-Heath, M., et al. (2015). Incorporating patient-preference evidence into regulatory decision making. Surgical Endoscopy, 29(10), 2984–2993.

    PubMed  Google Scholar 

  30. Hauber, B. A., Fairchild, A. O., & Johnson, R. F. (2013). Quantifying benefit-risk preferences for medical interventions: An overview of a growing empirical literature. Applied Health Economics and Health Policy, 11(4), 319–329.

    Google Scholar 

  31. van Til, J. A., & Ijzerman, M. J. (2014). Why should regulators consider using patient preferences in benefit-risk assessment? PharmacoEconomics, 32(1), 1–4.

    PubMed  Google Scholar 

  32. US Food and Drug Administration. (2018) Duchenne muscular dystrophy and related dystrophinopathies: Developing drugs for treatment Guidance for industry. Silver Spring: Center for Devices and Radiological Health, Center for Biologics Evaluation and Research, Food and Drug Administration. Retrieved March 17, 2018, from https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM450229.pdf.

  33. McNeil, D. E., Davis, C., Jillapalli, D., Targum, S., Durmowicz, A., & Coté, T. R. (2010). Duchenne muscular dystrophy: Drug development and regulatory considerations. Muscle and Nerve, 41(6), 740–745.

    CAS  PubMed  Google Scholar 

  34. Peay, H. L., Hollin, I. L., Fischer, R., & Bridges, J. F. P. (2014). A community-engaged approach to quantifying caregiver preferences for the benefits and risks of emerging therapies for Duchenne muscular dystrophy. Clinical Therapeutics, 36(5), 624–637.

    PubMed  Google Scholar 

  35. Peay, H. L., Sheffer, H., & Tibben, A. (2013). Expectations and decision making in clinical trials for Duchenne and Becker muscular dystrophy. In 18th international congress of the world muscle society, Asilomar.

  36. Finn, A., & Louviere, J. J. (1992). Determining the appropriate response to evidence of public concern: The case of food safety. Journal of Public Policy and Marketing, 11(2), 12–25.

    Google Scholar 

  37. Marley, A. A., & Louviere, J. J. (2005). Some probabilistic models of best, worst, and best-worst choices. Journal of Mathematical Psychology, 49(6), 464–480.

    Google Scholar 

  38. Flynn, T. N. (2010). Valuing citizen and patient preferences in health: Recent developments in three types of best-worst scaling. Expert Review of Pharmacoeconomics and Outcomes Research, 10(3), 259–267.

    PubMed  Google Scholar 

  39. Mühlbacher, A. C., Kaczynski, A., Zweifel, P., & Johnson, F. R. (2015). Experimental measurement of preferences in health and healthcare using best-worst scaling: An overview. Health Economics Review, 6(1), 1–14.

    Google Scholar 

  40. Flynn, T. N., Louviere, J. J., Peters, T. J., & Coast, J. (2007). Best-worst scaling: What it can do for health care research and how to do it. Journal of Health Economics, 26(1), 171–189.

    PubMed  Google Scholar 

  41. Flynn, T. N., & Marley, A. (2014). Best-worst scaling: Theory and methods. In S. Hess & A. Daly (Eds.), Handbook of choice modelling (pp. 178–201). Cheltenham: Edward Elgar Publishing Limited.

    Google Scholar 

  42. Hollin, I. L., Young, C., Hanson, C., Bridges, J., & Peay, H. (2016). Developing a patient-centered benefit-risk survey: A community-engaged process. Value in Health, 19, 751–757.

    PubMed  Google Scholar 

  43. Kuhfeld, W. (2010). Orthogonal arrays [TS-723]. Cary, NC: SAS.

    Google Scholar 

  44. Youden, W. J. (1940). Experimental designs to increase accuracy of greenhouse studies. Contributions. Boyce Thompson Institute for Plant Research, 11, 219–228.

    Google Scholar 

  45. Youden, W. J. (1937). Use of incomplete block replications in estimating tobacco-mosaic virus. Contributions from Boyce Thompson Institute, 9(1), 41–48.

    CAS  Google Scholar 

  46. Hauber, A. B., González, J. M., Groothuis-Oudshoorn, C. G., Prior, T., Marshall, D. A., Cunningham, C. et al. (2016). Statistical methods for the analysis of discrete choice experiments: A report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value in Health, 19(4):300–315.

    PubMed  Google Scholar 

  47. Deal, K. (2014). Segmenting patients and physicians using preferences from discrete choice experiments. The Patient, 7(1), 5–21.

    PubMed  Google Scholar 

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Acknowledgements

The authors appreciate the time and commitment of all the community members serving on the leadership, stakeholder, and review committees. We are indebted to all of the caregivers and individuals with Duchenne and Becker muscular dystrophy who participated in the survey. The authors wish to thank Caroline Hanson and Caroline Young for their help in designing the instrument and programming the data collection tool.

Funding

This study was funded by Parent Project Muscular Dystrophy (PPMD) (Grant Number 01212). Hollin and Bridges received support from a Grant (#01212) from Parent Project Muscular Dystrophy (PPMD). Peay was an employee of PPMD at the time of this research. PPMD received funding for this project from Santhera Pharmaceuticals. Bridges and Janssen also received support from a Patient-Centered Outcomes Research Institute (PCORI) Methods Program Award (ME-1303-5946) and through the Johns Hopkins-FDA Center for Excellence in Regulatory Science and Innovation (CERSI) (1U01FD004977-01). Peay currently receives support from a Patient-Centered Outcomes Research Institute (PCORI) PCORnet program award (PPRN-1306-04640-Phase II). Hollin is currently an employee of the National Pharmaceutical Council, although was not at the time of this research.

Author information

Authors and Affiliations

  1. University of Southern California Schaeffer Center for Health Economics and Policy, Los Angeles, CA, USA

    Ilene L. Hollin

  2. National Pharmaceutical Council, Washington, DC, USA

    Ilene L. Hollin

  3. RTI International, Center for Newborn Screening, Ethics, and Disability Studies, Research Triangle Park, NC, USA

    Holly Peay

  4. Parent Project Muscular Dystrophy, Hackensack, NJ, USA

    Ryan Fischer

  5. Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA

    Ellen M. Janssen & John F. P. Bridges

Authors
  1. Ilene L. Hollin
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  2. Holly Peay
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  3. Ryan Fischer
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  4. Ellen M. Janssen
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  5. John F. P. Bridges
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Corresponding author

Correspondence to Ilene L. Hollin.

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The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

Appendix

Appendix

See Table 5 and Figs. 3, 4.

Table 5 Regression results for logistic regression for probability of minority latent class (20%) membership
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Fig. 3
figure 3

Sample choice task used to elicit relative importance for symptoms as treatment targets

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Fig. 4
figure 4

Distributions of estimated individual posterior relative importance scores for symptoms

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Hollin, I.L., Peay, H., Fischer, R. et al. Engaging patients and caregivers in prioritizing symptoms impacting quality of life for Duchenne and Becker muscular dystrophy. Qual Life Res 27, 2261–2273 (2018). https://doi.org/10.1007/s11136-018-1891-7

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