Identifying and prioritizing concerns associated with prosthetic devices for use in a benefit-risk assessment: a mixed-methods approach

References

• Ziegler-Graham K, MacKenzie EJ, Ephraim PL, et al. Estimating the prevalence of limb loss in the United States: 2005 to 2050. Arch Phys Med Rehabil. 2008 Mar;89(3):422–429.
   PubMed Web of Science ®Google Scholar
• Beasley RW. General considerations in managing upper limb amputations. Orthop Clin North Am. 1981 Oct;12(4):743–749.
   PubMed Web of Science ®Google Scholar
• Behrend C, Reizner W, Marchessault JA, et al. Update on advances in upper extremity prosthetics. J Hand Surg Am. 2011 Oct;36(10):1711–1717.
   PubMedGoogle Scholar
• Center NLLI. Amputation statistics by cause. Limb loss in the United States. NLLIC fact sheet 2008 [cited 2017 Jun 15]. Available from: http://www.amputee-coalition.org/fact_sheets/amp_stats_cause.pdf
   Google Scholar
• Adams PF, Hendershot GE, Marano MA, et al. Current estimates from the National Health Interview Survey, 1996. Vital Health Stat. 1999 Oct;10(200):1–203.
   Google Scholar
• Krueger CA, Wenke JC, Ficke JR. Ten years at war: comprehensive analysis of amputation trends. J Trauma Acute Care Surg. 2012 Dec;73(6 Suppl 5):S438–444.
   PubMedGoogle Scholar
• Resnik L, Meucci MR, Lieberman-Klinger S, et al. Advanced upper limb prosthetic devices: implications for upper limb prosthetic rehabilitation. Arch Phys Med Rehabil. 2012 Apr;93(4):710–717.
   PubMed Web of Science ®Google Scholar
• Fraser CM. An evaluation of the use made of cosmetic and functional prostheses by unilateral upper limb amputees. Prosthet Orthot Int. 1998 Dec;22(3):216–223.
   PubMed Web of Science ®Google Scholar
• Pillet J, Didierjean-Pillet A. Aesthetic hand prosthesis: gadget or therapy? Presentation of a new classification. J Hand Surg Br. 2001 Dec;26(6):523–528.
   PubMedGoogle Scholar
• van Lunteren A, van Lunteren-Gerritsen GH, Stassen HG, et al. A field evaluation of arm prostheses for unilateral amputees. Prosthet Orthot Int. 1983 Dec;7(3):141–151.
   PubMed Web of Science ®Google Scholar
• Millstein SG, Heger H, Hunter GA. Prosthetic use in adult upper limb amputees: a comparison of the body powered and electrically powered prostheses. Prosthet Orthot Int. 1986 Apr;10(1):27–34.
   PubMed Web of Science ®Google Scholar
• Datta D, Kingston J, Ronald J. Myoelectric prostheses for below-elbow amputees: the Trent experience. Int Disabil Stud. 1989 Oct-Dec;11(4):167–170.
   PubMedGoogle Scholar
• Scotland TR, Galway HR. A long-term review of children with congenital and acquired upper limb deficiency. J Bone Joint Surg Br. 1983 May;65(3):346–349.
   PubMedGoogle Scholar
• Cordella F, Ciancio AL, Sacchetti R, et al. Literature review on needs of upper limb prosthesis users. Front Neurosci. 2016;10:209.
   PubMed Web of Science ®Google Scholar
• Sanchez J . Revolutionizing prosthetics: DARPA; [ cited 2017 June 15]. Available from: http://www.darpa.mil/program/revolutionizing-prosthetics
   Google Scholar
• Resnik L, Klinger SL, Etter K. The DEKA Arm: its features, functionality, and evolution during the Veterans Affairs Study to optimize the DEKA Arm. Prosthet Orthot Int. 2014 Dec;38(6):492–504..
   PubMed Web of Science ®Google Scholar
• Johannes MS, Bigelow JD, Burck JM, et al. An overview of the developmental process for the modular prosthetic limb. Johns Hopkins APL Tech Dig. 2011;30:207–216.
   Web of Science ®Google Scholar
• Resnik L, Borgia M, Latlief G, et al. Self-reported and performance-based outcomes using DEKA Arm. J Rehabil Res Dev. 2014;51(3):351–362.
   PubMedGoogle Scholar
• Baker JJ, Scheme E, Englehart K, et al. Continuous detection and decoding of dexterous finger flexions with implantable myoelectric sensors. IEEE Trans Neural Syst Rehabil Eng. 2010 Aug;18(4):424–432.
   PubMed Web of Science ®Google Scholar
• Ortiz-Catalan M, Branemark R, Hakansson B, et al. On the viability of implantable electrodes for the natural control of artificial limbs: review and discussion. Biomed Eng Online. 2012;11:33.
   PubMed Web of Science ®Google Scholar
• Jonsson S, Caine-Winterberger K, Branemark R. Osseointegration amputation prostheses on the upper limbs: methods, prosthetics and rehabilitation. Prosthet Orthot Int. 2011 Jun;35(2):190–200.
   PubMed Web of Science ®Google Scholar
• Clark GA, Wendelken S, Page DM, et al. Using multiple high-count electrode arrays in human median and ulnar nerves to restore sensorimotor function after previous transradial amputation of the hand. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:1977–1980.
   PubMedGoogle Scholar
• Horch K, Meek S, Taylor TG, et al. Object discrimination with an artificial hand using electrical stimulation of peripheral tactile and proprioceptive pathways with intrafascicular electrodes. IEEE Trans Neural Syst Rehabil Eng. 2011 Oct;19(5):483–489.
   PubMed Web of Science ®Google Scholar
• Raspopovic S, Capogrosso M, Petrini FM, et al. Restoring natural sensory feedback in real-time bidirectional hand prostheses. Sci Transl Med. 2014 Feb 05;6(222):222ra219..
   Web of Science ®Google Scholar
• Tan DW, Schiefer MA, Keith MW, et al. A neural interface provides long-term stable natural touch perception. Sci Transl Med. 2014 Oct 08;6(257):257ra138..
   PubMed Web of Science ®Google Scholar
• Tillander J, Hagberg K, Hagberg L, et al. Osseointegrated titanium implants for limb prostheses attachments: infectious complications. Clin Orthop Relat Res. 2010 Oct;468(10):2781–2788.
   PubMed Web of Science ®Google Scholar
• Weber D Hand Proprioception and Touch Interfaces (HAPTIX): DARPA; [ cited 2017 June 29]. Available from: http://www.darpa.mil/program/hand-proprioception-and-touch-interfaces
   Google Scholar
• Ho M, Saha A, McCleary KK, et al. A framework for incorporating patient preferences regarding benefits and risks into regulatory assessment of medical technologies. Value Health. 2016 Sep - Oct;19(6):746–750.
   PubMed Web of Science ®Google Scholar
• Ho MP, Gonzalez JM, Lerner HP, et al. Incorporating patient-preference evidence into regulatory decision making. Surg Endosc. 2015;29(10):2984–2993.
   PubMed Web of Science ®Google Scholar
• United States Food and Drug Administration (FDA). 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 2016 [cited 2017 Aug 23]. Available from: http://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm446680.pdf
   Google Scholar
• United States Food and Drug Administration (FDA). Factors to consider when making benefit-risk determinations in medical device premarket approval and de novo classification. FDA Guidance for Industry and Food and Drug Administration Staff Silver Spring, Maryland: Center for Device and Radiological Health. Center for Biologics Evaluation and Research; 2012 [cited 2017 Nov 1]. Available from: https://www.fda.gov/downloads/medicaldevices/deviceregulationandguidance/guidancedocuments/ucm517504.pdf.
   Google Scholar
• United States Food and Drug Administration (FDA). Factors to Consider When Making Benefit-Risk Determinations for Medical Device Investigational Device Exemptions 2017 [cited 2017 Nov 1]. Available from: https://www.fda.gov/downloads/MedicalDevices/DeviceRegulationandGuidance/GuidanceDocuments/UCM451440.pdf
   Google Scholar
• Bridges JF, Hauber AB, Marshall D, et al. Conjoint analysis applications in health–a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. Value Health. 2011 Jun;14(4):403–413.
   PubMed Web of Science ®Google Scholar
• Janssen EM, Segal JB, Bridges JFA. Framework for instrument development of a choice experiment: an application to type 2 diabetes. Patient. 2016 Oct;9(5):465–479.
   PubMedGoogle Scholar
• Coast J, Al-Janabi H, Sutton EJ, et al. Using qualitative methods for attribute development for discrete choice experiments: issues and recommendations. Health Econ. 2012;21(6):730–741.
   PubMed Web of Science ®Google Scholar
• Johnson FR, Lancsar E, Marshall D, et al. Constructing experimental designs for discrete-choice experiments: report of the ISPOR Conjoint Analysis Experimental Design Good Research Practices Task Force. Value Health. 2013;16(1):3–13.
   PubMed Web of Science ®Google Scholar
• Hauber AB, Gonzalez JM, Groothuis-Oudshoorn CG, et al. Statistical methods for the analysis of discrete choice experiments: a report of the ISPOR Conjoint Analysis Good Research Practices Task Force. Value Health. 2016 Jun;19(4):300–315.
   PubMed Web of Science ®Google Scholar
• Flynn TN, Louviere JJ, Peters TJ, et al. Best-worst scaling: what it can do for health care research and how to do it. J Health Econ. 2007;26. doi: 10.1016/j.jhealeco.2006.04.002.
   Web of Science ®Google Scholar
• Peay HL, Hollin I, Fischer R, et al. A community-engaged approach to quantifying caregiver preferences for the benefits and risks of emerging therapies for Duchenne muscular dystrophy. Clin Ther. 2014 May 01;36(5):624–637.
   PubMed Web of Science ®Google Scholar
• Hollin IL, Caroline Y, Hanson C, et al. Developing a patient-centered benefit-risk survey: a community-engaged process. Value Health. 2016 Sep - Oct;19(6):751–757.
   PubMed Web of Science ®Google Scholar
• Benz HL, Yao J, Rose L, et al. Upper extremity prosthesis user perspectives on unmet needs and innovative technology. 2016 38th Annu Int Conf IEEE Eng Med Biol Soc (EMBC. 2016;16–20(Aug):2016.
   Google Scholar
• United States Food and Drug Administration (FDA). Public Workshop - CDRH Veteran Amputee Device Workshop, October 31, 2016 2016 [cited 2017 Nov 1]. Available from: https://www.fda.gov/MedicalDevices/NewsEvents/WorkshopsConferences/ucm523312.htm
   Google Scholar
• Flynn TN. Valuing citizen and patient preferences in health: recent developments in three types of best-worst scaling. Expert Rev of Pharmacoeconomics and Outcomes Res. 2010 June;10(3):259–267.
   PubMed Web of Science ®Google Scholar
• McFadden D. Conditional logit analysis of qualitative choice behavior. In: Zarembka P, editor. Frontiers in econometrics. New York: Academic Press; 1974. p. 105–142.
   Google Scholar
• Louviere JJ, Islam T. Comparison of importance weights and willingness-to-pay measures derived from choice-based conjoint, constant sum scales and best-worst scaling. J Bus Res. 2008 Sep;61(9):903–911.
   Web of Science ®Google Scholar
• Flynn TN, Louviere JJ, Peters TJ, et al. Best-worst scaling: what it can do for health care research and how to do it. J Health Econ. 2007 January;26(1):171–189.
   PubMed Web of Science ®Google Scholar
• Erdem S, Rigby D. Investigating heterogeneity in the characterization of risks using best worst scaling. Risk Anal. 2013 September;33(9):1728–1748.
   PubMed Web of Science ®Google Scholar
• Ross M, Bridges JFP, Ng X, et al. A best-worst scaling experiment to prioritize caregiver concerns about attention-deficit/hyperactivity disorder (ADHD) medication for children. Psychiatr Serv. 2015 Feb 1;66(2):208–211.
   PubMed Web of Science ®Google Scholar
• Marley AAJ, Louviere JJ. Some probabilistic models of best, worst, and best-worst choices. J Math Psychol. 2005;49. doi: 10.1016/j.jmp.2005.05.003
   Web of Science ®Google Scholar
• Flynn TN, Louviere JJ, Peters TJ, et al. Estimating preferences for a dermatology consultation using best-worst scaling: comparison of various methods of analysis. BMC Med Res Methodol. 2008;8:76.
   PubMed Web of Science ®Google Scholar
• Bech M, Gyrd-Hansen D. Effects coding in discrete choice experiments. Health Econ. 2005 Oct;14(10):1079–1083.
   PubMed Web of Science ®Google Scholar
• Sawtooth Software. The MaxDiff System Technical Paper 2013 [ cited2018 April 29]. Available from: https://www.sawtoothsoftware.com/download/techpap/maxdifftech.pdf
   Google Scholar
• Ritchie S, Wiggins S, Sanford A. Perceptions of cosmesis and function in adults with upper limb prostheses: a systematic literature review. Prosthet Orthot Int. 2011;35(4):332–341.
   PubMed Web of Science ®Google Scholar
• Biddiss EA, Chau TT. Upper limb prosthesis use and abandonment: a survey of the last 25 years. Prosthet Orthot Int. 2007 Sep;31(3):236–257.
   PubMed Web of Science ®Google Scholar
• Peay HL, Hollin IL, Bridges JFP. Prioritizing parental worry associated with Duchenne muscular dystrophy using best-worst scaling [journal article]. J Genet Couns. 2016;25(2):305–313.
   PubMed Web of Science ®Google Scholar
• Lagerkvist CJ, Okello J, Karanja N. Anchored vs. relative best–worst scaling and latent class vs. hierarchical Bayesian analysis of best–worst choice data: investigating the importance of food quality attributes in a developing country. Food Qual Prefer. 2012;25(1):29–40. [2012 Jul 01].
   Web of Science ®Google Scholar
• Ryan M. Using consumer preferences in health care decision making. The application of conjoint analysis. London: Office of Health Economics; 1996.
   Google Scholar
• Cheung KL, Wijnen BFM, Hollin IL, et al. Using best–worst scaling to investigate preferences in health care. PharmacoEconomics. 2016;34(12):1195–1209.
   PubMed Web of Science ®Google Scholar
• Clark MD, Determann D, Petrou S, et al. Discrete choice experiments in health economics: a review of the literature. Pharmacoeconomics. 2014 Sep;32(9):883–902.
   PubMed Web of Science ®Google Scholar
• Flynn TN, Louviere JJ, Marley AA, et al. Rescaling quality of life values from discrete choice experiments for use as QALYs: a cautionary tale. Popul Health Metr. 2008;6(1):6.
   PubMedGoogle Scholar
• Janssen EM, Hauber AB, Bridges JFP. Conducting a discrete choice experiment study following recommendations for good research practices: an application for eliciting patient preferences for diabetes treatments. Value in Health. 2017;21(1):59–68.
   PubMed Web of Science ®Google Scholar