Skip to main content

Advertisement

SpringerLink
Log in

From Meaningful Outcomes to Meaningful Change Thresholds: A Path to Progress for Establishing Digital Endpoints

  • Analytical Report
  • Published:
Therapeutic Innovation & Regulatory Science Aims and scope Submit manuscript

Abstract

This paper examines the use of digital endpoints (DEs) derived from digital health technologies (DHTs), focusing primarily on the specific considerations regarding the determination of meaningful change thresholds (MCT). Using DHTs in drug development is becoming more commonplace. There is general acceptance of the value of DHTs supporting patient-centric trial design, capturing data outside the traditional clinical trial setting, and generating DEs with the potential to be more sensitive to change than conventional assessments. However, the transition from exploratory endpoints to primary and secondary endpoints capable of supporting labeling claims requires these endpoints to be substantive with reproducible population-specific values. Meaningful change represents the amount of change in an endpoint measure perceived as important to patients and should be determined for each digital endpoint and given population under consideration. This paper examines existing approaches to determine meaningful change thresholds and explores examples of these methodologies and their use as part of DE development: emphasizing the importance of determining what aspects of health are important to patients and ensuring the DE captures these concepts of interest and aligns with the overarching endpoint strategy. Examples are drawn from published DE qualification documentation and responses to qualification submissions under review by the various regulatory authorities. It is the hope that these insights will inform and strengthen the development and validation of DEs as drug development tools, particularly for those new to the approaches to determine MCTs.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure. 1
Figure. 2
Figure. 3

Data Availability

There are no data to share outside of the manuscript. All information is already contained in the manuscript, including supplemental material.

References

  1. Walton MK, Powers JH, Hobart J, Patrick D, Marquis P, Vamvakas S, et al. Clinical outcome assessments: conceptual foundation-report of the ISPOR clinical outcomes assessment-emerging good practices for outcomes research task force. Value in Health. 2015;18(6):741–52.

    Article  PubMed  PubMed Central  Google Scholar 

  2. US Department of Health and Human Services, Food and Drug Administration. Digital Health Technologies for Remote Data Acquisition in Clinical Investigations Guidance for Industry, Investigators, and Other Stakeholders DRAFT GUIDANCE [Internet]. FDA; 2021 Dec. Report No.: 2021–27894. Available from: https://www.fda.gov/media/155022/download

  3. Clinical Outcome Assessments (COA) Qualification Program Submissions | FDA [Internet]. [cited 2022 the 4th of November]. Available from: https://www.fda.gov/drugs/clinical-outcome-assessment-coa-qualification-program/clinical-outcome-assessments-coa-qualification-program-submissions

  4. Diao JA, Raza MM, Venkatesh KP, Kvedar JC. Watching Parkinson’s disease with wrist-based sensors. NPJ Digit Med. 2022;5(1):73.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Bloem BR, Marks WJ, Silva De Lima AL, Kuijf ML, van Laar T, Jacobs BPF, et al. The personalized Parkinson project: examining disease progression through broad biomarkers in early Parkinson’s disease. BMC Neurol. 2019;19(1):1.

    Article  Google Scholar 

  6. Roussos G, Herrero TR, Hill DL, Dowling A, v., Müller MLTM, Evers LJW, et al. Identifying and characterising sources of variability in digital outcome measures in Parkinson’s disease. npj Digital Medicine. 2022;5(1):1–10.

    Article  Google Scholar 

  7. European Medicines Agency. Committee for Medicinal Products for Human Use (CHMP) Qualification opinion on stride velocity 95th centile as a secondary endpoint in Duchenne Muscular Dystrophy measured by a valid and suitable wearable device*. 2019 [cited 2022 the 5th of November]; Available from: www.ema.europa.eu/contact

  8. European Medicines Agency. Qualification opinion on proactive in COPD. Available: https://www.ema.europa.eu/en/documents/regulatory-procedural-guideline/ qualification-opinion-proactive-chronic-obstructive-pulmonary-disease-copd_en.pdf [Accessed 2022 the 5th of November]

  9. Servais L, Yen K, Guridi M, Lukawy J, Vissière D, Strijbos P. Stride velocity 95th centile: insights into gaining regulatory qualification of the first wearable-derived digital endpoint for use in duchenne muscular dystrophy trials. J Neuromuscul Dis. 2022;9(2):335–46.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Servais L, Camino E, Clement A, McDonald CM, Lukawy J, Lowes LP, et al. First regulatory qualification of a novel digital endpoint in duchenne muscular dystrophy: a multi-stakeholder perspective on the impact for patients and for drug development in neuromuscular diseases. Digit Biomark. 2021;5(2):183–90.

    Article  PubMed  PubMed Central  Google Scholar 

  11. A Gene Transfer Therapy Study to Evaluate the Safety and Efficacy of SRP-9001 (Delandistrogene Moxeparvovec) in Participants With Duchenne Muscular Dystrophy (DMD) - Full Text View - ClinicalTrials.gov [Internet]. [cited 2022 the 10th of November]. Available from: https://clinicaltrials.gov/ct2/show/NCT05096221?term=SV95C&draw=2&rank=1

  12. Nathan SD, Flaherty KR, Glassberg MK, Raghu G, Swigris J, Alvarez R, et al. A Randomized, double-blind, placebo-controlled study of pulsed, inhaled nitric oxide in subjects at risk of pulmonary hypertension associated with pulmonary fibrosis. Chest. 2020;158(2):637–45.

    Article  CAS  PubMed  Google Scholar 

  13. King CS, Flaherty KR, Glassberg MK, Lancaster L, Raghu G, Swigris JJ, et al. A phase-2 exploratory randomized controlled trial of INOpulse in patients with fibrotic interstitial lung disease requiring oxygen. Ann Am Thorac Soc. 2022;19(4):594–602.

    Article  PubMed  Google Scholar 

  14. Bellerophon Announces Agreement with the FDA on its Planned Pivotal Phase 3 Study for the Treatment of Pulmonary Hypertension Associated with Pulmonary Fibrosis | Bellerophon Therapeutics, Inc. [Internet]. [cited 2022 the 10th of November]. Available from: https://investors.bellerophon.com/news-releases/news-release-details/bellerophon-announces-agreement-fda-its-planned-pivotal-phase-3

  15. Bellerophon Announces FDA Acceptance of Change to Ongoing Phase 3 REBUILD Study of INOpulse® for Treatment of Fibrotic Interstitial Lung Disease | Bellerophon Therapeutics, Inc. [Internet]. [cited 2022 the 10th of November]. Available from: https://investors.bellerophon.com/news-releases/news-release-details/bellerophon-announces-fda-acceptance-change-ongoing-phase-3

  16. Goldsack JC, Coravos A, Bakker JP, Bent B, Dowling AV, Fitzer-Attas C, et al. Verification, analytical validation, and clinical validation (V3): the foundation of determining fit-for-purpose for biometric monitoring technologies (BioMeTs). NPJ Digit Med. 2020;3(1):55.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Richardson E, Burnell J, Adams HR, Bohannon RW, Bush EN, Campbell M, et al. Developing and implementing performance outcome assessments: evidentiary, methodologic, and operational considerations. Ther Innov Regul Sci. 2019;53(1):146–53.

    Article  PubMed  Google Scholar 

  18. Powers JH, Patrick DL, Walton MK, Marquis P, Cano S, Hobart J, et al. Clinician-reported outcome assessments of treatment benefit: report of the ISPOR clinical outcome assessment emerging good practices task force. Value Health. 2017;20(1):2–14.

    Article  PubMed  PubMed Central  Google Scholar 

  19. US Food and Drug Administration. PATIENT-FOCUSED DRUG DEVELOPMENT GUIDANCE PUBLIC WORKSHOP Incorporating Clinical Outcome Assessments into Endpoints for Regulatory Decision-Making [Internet]. FDA; 2019. Report No.: 2019–24726. Available from: https://www.fda.gov/media/132505/download

  20. US Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER) Center for Biologics Evaluation and Research (CBER), Center for Devices and Radiological Health (CDRH). Guidance for industry-Patient-reported outcome measures: Use in medical product development to support labeling claims [Internet]. US Department of Health and Human Services, Food and Drug Administration; 2009 Dec. Available from: https://www.fda.gov/media/77832/download

  21. Byrom B, Breedon P, Tulkki-Wilke R, Platko J. Meaningful change: Defining the interpretability of changes in endpoints derived from interactive and mHealth technologies in healthcare and clinical research. J Rehabil Assist Technol Eng. 2020;7:205566831989277.

    Google Scholar 

  22. European Medicines Agency. Questions and answers: Qualification of digital technology-based methodologies to support approval of medicinal products. [cited 2022 the 4th of November]; Available from: www.ema.europa.eu/contact

  23. Kruizinga MD, Stuurman FE, Exadaktylos V, Doll RJ, Stephenson DT, Groeneveld GJ, et al. Development of novel, value-based, digital endpoints for clinical trials: a structured approach toward fit-for-purpose validation. Pharmacol Rev. 2020;72(4):899–909.

    Article  CAS  PubMed  Google Scholar 

  24. Walton MK, Cappelleri JC, Byrom B, Goldsack JC, Eremenco S, Harris D, et al. Considerations for development of an evidence dossier to support the use of mobile sensor technology for clinical outcome assessments in clinical trials. Contemp Clin Trials. 2020;91:105962.

    Article  CAS  PubMed  Google Scholar 

  25. Taylor KI, Staunton H, Lipsmeier F, Nobbs D, Lindemann M. Outcome measures based on digital health technology sensor data: data- and patient-centric approaches. NPJ Digit Med. 2022;3(1):97.

    Article  Google Scholar 

  26. Byrom B, Watson C, Doll H, Coons SJ, Eremenco S, Ballinger R, et al. selection of and evidentiary considerations for wearable devices and their measurements for use in regulatory decision making: recommendations from the ePRO consortium. Value in Health. 2018;21(6):631–9.

    Article  PubMed  Google Scholar 

  27. Cappelleri JC, Bushmakin AG. Interpretation of patient-reported outcomes. Stat Methods Med Res. 2014;23(5):460–83.

    Article  PubMed  Google Scholar 

  28. King MT. A point of minimal important difference (MID): a critique of terminology and methods. Expert Rev Pharmacoecon Outcomes Res. 2011;11(2):171–84.

    Article  PubMed  Google Scholar 

  29. Revicki D, Hays RD, Cella D, Sloan J. Recommended methods for determining responsiveness and minimally important differences for patient-reported outcomes. J Clin Epidemiol. 2008;61(2):102–9.

    Article  PubMed  Google Scholar 

  30. Mamolo CM, Bushmakin AG, Cappelleri JC. Application of the Itch severity score in patients with moderate-to-severe plaque psoriasis: clinically important difference and responder analyses. J Dermatolog Treat. 2015;26(2):121–3.

    Article  PubMed  Google Scholar 

  31. Conaghan PG, Dworkin RH, Schnitzer TJ, Berenbaum F, Bushmakin AG, Cappelleri JC, et al. WOMAC meaningful within-patient change: results from 3 studies of Tanezumab in patients with moderate-to-severe osteoarthritis of the hip or knee. J Rheumatol. 2022;49(6):615–21.

    Article  CAS  PubMed  Google Scholar 

  32. Terwee CB, Peipert JD, Chapman R, Lai JS, Terluin B, Cella D, et al. Minimal important change (MIC): a conceptual clarification and systematic review of MIC estimates of PROMIS measures. Qual Life Res. 2021;30(10):2729–54.

    Article  PubMed  PubMed Central  Google Scholar 

  33. Staunton H, Willgoss T, Nelsen L, Burbridge C, Sully K, Rofail D, et al. An overview of using qualitative techniques to explore and define estimates of clinically important change on clinical outcome assessments. J Patient Rep Outcomes. 2019;3(1):1.

    Article  Google Scholar 

  34. McLeod LD, Cappelleri JC, Hays RD. Best (but oft-forgotten) practices: expressing and interpreting associations and effect sizes in clinical outcome assessments. Am J Clin Nutr. 2016;103(3):685–93.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Crosby RD, Kolotkin RL, Williams GR. Defining clinically meaningful change in health-related quality of life. J Clin Epidemiol. 2003;56(5):395–407.

    Article  PubMed  Google Scholar 

  36. Coon CD, Cook KF. Moving from significance to real-world meaning: methods for interpreting change in clinical outcome assessment scores. Qual Life Res. 2018;27(1):33–40.

    Article  PubMed  Google Scholar 

  37. Bushmakin AG, Cappelleri JC. A Practical Approach to Quantitative Validation of Patient-Reported Outcomes: A Simulation-Based Guide Using SAS. | Goodreads [Internet]. [cited 2022 the 4th of November]. Available from: https://www.goodreads.com/book/show/60196679-a-practical-approach-to-quantitative-validation-of-patient-reported-outc

  38. European Medicines Agency. Qualification of novel methodologies for medicine development [Internet]. [cited 2022 the 4th of November]. Available from: https://www.ema.europa.eu/en/human-regulatory/research-development/scientific-advice-protocol-assistance/qualification-novel-methodologies-medicine-development-0

  39. Library of Digital Endpoints – Digital Medicine Society (DiMe) [Internet]. [cited 2022 the 4th of November]. Available from: https://www.dimesociety.org/get-involved/library-of-digital-endpoints/

  40. Manta C, Patrick-Lake B, Goldsack JC. Digital measures that matter to patients: a framework to guide the selection and development of digital measures of health. Digit Biomark. 2020;4(3):69–77.

    Article  PubMed  PubMed Central  Google Scholar 

  41. FDA-NIH Biomarker Working Group. BEST (Biomarkers, EndpointS, and other Tools) Resource. Silver Spring (MD): Food and Drug Administration (US); 2016-. https://www.ncbi.nlm.nih.gov/books/NBK326791/ Co-published by National Institutes of Health (US), Bethesda (MD). Accessed the 20th of November 2020

  42. FDA, CDER. Patient-Focused Drug Development: Methods to Identify What Is Important to Patients Guidance for Industry, Food and Drug Administration Staff, and Other Stakeholders. 2022 [cited 2022 the 4th of November]; Available from: https://www.fda.gov/drugs/guidance-compliance-regulatory-information/guidances-drugsand/or

  43. FDA, CDER. Patient-Focused Drug Development: Selecting, Developing, or Modifying Fit-for-Purpose Clinical Outcome Assessments Guidance for Industry, Food and Drug Administration Staff, and Other Stakeholders DRAFT GUIDANCE. 2022 [cited 2022 the 4th of November]; Available from: https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-regulatory-information-biologics/biologics-guidances

  44. de Vet HCW, Terwee CB. The minimal detectable change should not replace the minimal important difference. J Clin Epidemiol. 2010;63(7):804–5.

    Article  PubMed  Google Scholar 

  45. Lauritzen J, Muñoz A, Luis Sevillano J, Civit A. The usefulness of activity trackers in elderly with reduced mobility: a case study. Stud Health Technol Inform. 2013;192(1–2):759–62. https://doi.org/10.3233/978-1-61499-289-9-759.

    Article  PubMed  Google Scholar 

  46. Cyarto EV, Myers A, Tudor-Locke C. Pedometer accuracy in nursing home and community-dwelling older adults. Med Sci Sports Exerc. 2004;36(2):205–9.

    Article  PubMed  Google Scholar 

  47. FDA, CDER, CBER. E9(R1) Statistical Principles for Clinical Trials: Addendum: Estimands and Sensitivity Analysis in Clinical Trials. Guidance for Industry. 2021 [cited 2022 the 6th of November]; Available from: https://www.fda.gov/vaccines-blood-biologics/guidance-compliance-regulatory-information-biologics/biologics-guidances

  48. Jin M, Liu G. Estimand framework: delineating what to be estimated with clinical questions of interest in clinical trials. Contemp Clin Trials. 2020;96:106093.

    Article  PubMed  Google Scholar 

  49. Russell C, McCarthy M, Cappelleri JC, Wong S. Choosing a mobile sensor technology for a clinical trial: statistical considerations, developments and learnings. Ther Innov Regul Sci. 2021;55(1):38–47.

    Article  PubMed  Google Scholar 

  50. Byrom B, Rowe DA. Measuring free-living physical activity in COPD patients: deriving methodology standards for clinical trials through a review of research studies. Contemp Clin Trials. 2016;47:172–84.

    Article  PubMed  Google Scholar 

  51. McCarthy M, Bury DP, Byrom B, Geoghegan C, Wong S. Determining minimum wear time for mobile sensor technology. Ther Innov Regul Sci. 2022;155(1):33–7.

    Article  Google Scholar 

  52. Di J, Demanuele C, Kettermann A, Karahanoglu FI, Cappelleri JC, Potter A, et al. Considerations to address missing data when deriving clinical trial endpoints from digital health technologies. Contemp Clin Trials. 2022;113:106661.

    Article  PubMed  Google Scholar 

  53. Gimeno-Santos E, Raste Y, Demeyer H, Louvaris Z, de Jong C, Rabinovich RA, et al. The PROactive instruments to measure physical activity in patients with chronic obstructive pulmonary disease. Eur Respir J. 2015;46(4):988.

    Article  PubMed  PubMed Central  Google Scholar 

  54. Rochester L, Mazzà C, Mueller A, Caulfield B, McCarthy M, Becker C, et al. A Roadmap to inform development, validation and approval of digital mobility outcomes: the mobilise-d approach. Digit Biomark. 2020;4(Suppl 1):13.

    Article  PubMed  PubMed Central  Google Scholar 

  55. Farivar SS, Liu H, Hays RD. Half standard deviation estimate of the minimally important difference in HRQOL scores? Expert Rev Pharmacoecon Outcomes Res. 2004;4(5):515–23.

    Article  PubMed  Google Scholar 

  56. Demeyer H, Hornikx M, Marcal Camillo CA, van Remoortel H, Burtin C, Langer D, et al. An estimation of the minimal important difference in physical activity for patients with COPD. European Respiratory Journal. 2014;44(Suppl 58).

  57. Cder FDA. Guidance for industry: patient-reported outcome measures: use in medical product development to support labeling claims: draft guidance. Health Qual Life Outcomes. 2006;4:79.

    Article  Google Scholar 

  58. Teylan M, Kantorowski A, Homsy D, Kadri R, Richardson C, Moy M. Physical activity in COPD: minimal clinically important difference for medical events. Chron Respir Dis. 2019;16:1479973118816424.

    Article  PubMed  Google Scholar 

  59. Motl RW, Pilutti LA, Learmonth YC, Goldman MD, Brown T. Clinical importance of steps taken per day among persons with multiple sclerosis. PLoS One. 2013;8(9):e73247.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Li YC, Liao WW, Hsieh YW, Lin KC, Chen CL. Important changes in actual and perceived functional arm use of the affected upper limb after rehabilitative therapy in chronic stroke. Arch Phys Med Arch Phys Med Rehabil. 2020;101(3):442–9.

    Article  PubMed  Google Scholar 

  61. Shoemaker MJ, Curtis AB, Vangsnes E, Dickinson MG. Clinically meaningful change estimates for the six-minute walk test and daily activity in individuals with chronic heart failure. Cardiopulm Phys Ther J. 2013;24(3):21.

    Article  PubMed  PubMed Central  Google Scholar 

  62. Shoemaker MJ, Curtis AB, Vangsnes E, Dickinson MG, Paul R. Analysis of daily activity data from implanted cardiac defibrillators: the minimum clinically important difference and relationship to mortality/life expectancy. World J Cardiovasc Dis. 2012;02(03):129–35.

    Article  Google Scholar 

  63. Polgar O, Patel S, Walsh JA, Barker RE, Clarke SF, Man WDC, et al. Minimal clinically important difference for daily pedometer step count in COPD. ERJ Open Res. 2021;7(1):00823–2020.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Mouelhi Y, Jouve E, Castelli C, Gentile S. How is the minimal clinically important difference established in health-related quality of life instruments? Review of anchors and methods. Health Qual Life Outcomes. 2020;18(1):1–7.

    Article  Google Scholar 

  65. Trigg A, Griffiths P. Triangulation of multiple meaningful change thresholds for patient-reported outcome scores. Qual Life Res. 2021;30(10):2755–64.

    Article  PubMed  Google Scholar 

  66. Brigden A, Parslow RM, Gaunt D, Collin SM, Jones A, Crawley E. Defining the minimally clinically important difference of the SF-36 physical function subscale for paediatric CFS/ME: triangulation using three different methods. Health Qual Life Outcomes. 2018;16(1):1–7.

    Article  Google Scholar 

  67. Chen HL, Lin KC, Hsieh YW, Wu CY, Liing RJ, Chen CL. A study of predictive validity, responsiveness, and minimal clinically important difference of arm accelerometer in real-world activity of patients with chronic stroke. Clin Rehabil. 2018;32(1):75–83.

    Article  PubMed  Google Scholar 

  68. Hur SA, Guler SA, Khalil N, Camp PG, Guenette JA, Swigris JJ, et al. Minimal important difference for physical activity and validity of the international physical activity questionnaire in interstitial lung disease. Ann Am Thorac Soc. 2019;16(1):107–15.

    Article  PubMed  Google Scholar 

  69. Rowlands A, Davies M, Dempsey P, Edwardson C, Razieh C, Yates T. Wrist-worn accelerometers: recommending ~1.0 m g as the minimum clinically important difference (MCID) in daily average acceleration for inactive adults. Br J Sports Med. 2021;55(14):814–5.

    Article  PubMed  Google Scholar 

  70. Hays RD, Peipert JD. Between-group minimally important change versus individual treatment responders. Qual Life Res. 2021;30(10):2765–72.

    Article  PubMed  PubMed Central  Google Scholar 

  71. Demeyer H, Burtin C, Hornikx M, Camillo CA, van Remoortel H, Langer D, et al. The Minimal important difference in physical activity in patients with COPD. PLoS One. 2016;11(4):e0154587.

    Article  PubMed  PubMed Central  Google Scholar 

  72. McDonald CM, Sajeev G, Yao Z, McDonnell E, Elfring G, Souza M, et al. Deflazacort vs prednisone treatment for Duchenne muscular dystrophy: a meta-analysis of disease progression rates in recent multicenter clinical trials. Muscle Nerve. 2020;61(1):26–35.

    Article  CAS  PubMed  Google Scholar 

  73. Haberkamp M, Moseley J, Athanasiou D, de Andres-Trelles F, Elferink A, Rosa MM, et al. European regulators’ views on a wearable-derived performance measurement of ambulation for Duchenne muscular dystrophy regulatory trials. Neuromuscul Disord. 2019;29(7):514–6.

    Article  PubMed  Google Scholar 

  74. Garcia-Aymerich J, Puhan MA, Corriol-Rohou S, de Jong C, Demeyer H, Dobbels F, et al. validity and responsiveness of the daily- and clinical visit-PROactive physical activity in COPD (D-PPAC and C-PPAC) instruments. Thorax. 2021;76(3):228–38.

    Article  PubMed  Google Scholar 

  75. López-Nava IH, Arnrich B, Muñoz-Meléndez A, Güneysu A. Variability analysis of therapeutic movements using wearable inertial sensors. J Med Syst. 2017;41:1–9.

    Article  Google Scholar 

Download references

Acknowledgements

This manuscript was developed by the DIA Meaningful Change Working Group, a cross-community collaboration between the Study Endpoints, Statistics & Data Sciences, and Clinical Research Communities. This working group has four workstreams focused on developing a unifying framework of meaningful change terminology and glossary of terms (workstream 1), examining evidentiary expectations and potential uses of meaningful change data among various stakeholders (workstream 2), facilitating a clear path forward for establishing thresholds for robust measurement of meaningful change (workstream 3) and exploring how to define, measure and interpret meaningful change data with digital endpoints (workstream 4). This manuscript is part of workstream 4.Thanks to Working Group 1 for their assistance in the alignment of the terminology used in the manuscript; Emuella Flood, Jammbe Musoro, Bellinda King-Kallimanis, Benoit Arnould, Celeste Elash, Sandra Nolte, Caroline Ward, and Sonya Eremenco. A very special thanks to Matthew Reaney, Cheryl D Coons, and Helen Doll for their detailed manuscript review, edits, and support.

Funding

No financial support beyond the author’s employment is reported for this publication.

Author information

Authors and Affiliations

  1. Novartis Ireland Ltd, Dublin, D04A9N6, Co Dublin, Ireland

    Marie Mc Carthy

  2. Burrows Consulting, London, UK

    Kate Burrows

  3. IQVIA France, Courbevoie, France

    Pip Griffiths

  4. PMB Consulting, Austin, TX, USA

    Peter M. Black

  5. Pfizer Inc, Cambridge, MA, USA

    Charmaine Demanuele

  6. AstraZeneca, Gothenburg, Sweden

    Niklas Karlsson

  7. AstraZeneca, Gaithersburg, MD, USA

    Joan Buenconsejo & Nikunj Patel

  8. GlaxoSmithKline, Collegeville, PA, USA

    Wen-Hung Chen

  9. Pfizer Inc, Groton, CT, USA

    Joseph C. Cappelleri

Authors
  1. Marie Mc Carthy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Kate Burrows
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pip Griffiths
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter M. Black
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Charmaine Demanuele
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Niklas Karlsson
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Joan Buenconsejo
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Nikunj Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Wen-Hung Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Joseph C. Cappelleri
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MMCC, KB, PMB, CD, NK, JB, NP, and WH contributed equally to the conception of this manuscript. MMcC, KB, PG, CD, and JC drafted and revised different versions of the manuscript. MMcC, KB, PMB, NK, CD, JC, and JB reviewed the manuscript. MMCC, KB, JC, NK, PMB, CD, W-HC approved the final version.

Corresponding author

Correspondence to Marie Mc Carthy.

Ethics declarations

Conflict of interest

M Mc Carthy is an employee of Novartis Ireland Ltd and may own company stock. Joseph C. Cappelleri and Charmaine Demanuele are employees and stockholders of Pfizer Inc. P. Griffiths is an employee of IQVIA France. N. Karlsson is an employee and stockholder of AstraZeneca. J. Buenconsejo contributed to this work while she was an employee of AstraZeneca. She is now an employee and a stockholder of Bristol Myers Squibb. N Patel is an employee and a stockholder of AstraZeneca.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mc Carthy, M., Burrows, K., Griffiths, P. et al. From Meaningful Outcomes to Meaningful Change Thresholds: A Path to Progress for Establishing Digital Endpoints. Ther Innov Regul Sci 57, 629–645 (2023). https://doi.org/10.1007/s43441-023-00502-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s43441-023-00502-8

Keywords

Search

Navigation