IDEAS home Printed from https://ideas.repec.org/a/sae/medema/v37y2017i4p469-480.html
   My bibliography  Save this article

Development of the Galaxy Chronic Obstructive Pulmonary Disease (COPD) Model Using Data from ECLIPSE: Internal Validation of a Linked-Equations Cohort Model

Author

Listed:
  • Andrew H. Briggs
  • Timothy Baker
  • Nancy A. Risebrough
  • Mike Chambers
  • Sebastian Gonzalez-McQuire
  • Afisi S. Ismaila
  • Alex Exuzides
  • Chris Colby
  • Maggie Tabberer
  • Hana Muellerova
  • Nicholas Locantore
  • Maureen P. M. H. Rutten�van Mölken
  • David A. Lomas

Abstract

Background . The recent joint International Society for Pharmacoeconomics and Outcomes Research / Society for Medical Decision Making Modeling Good Research Practices Task Force emphasized the importance of conceptualizing and validating models. We report a new model of chronic obstructive pulmonary disease (COPD) (part of the Galaxy project) founded on a conceptual model, implemented using a novel linked-equation approach, and internally validated. Methods . An expert panel developed a conceptual model including causal relationships between disease attributes, progression, and final outcomes. Risk equations describing these relationships were estimated using data from the Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints (ECLIPSE) study, with costs estimated from the TOwards a Revolution in COPD Health (TORCH) study. Implementation as a linked-equation model enabled direct estimation of health service costs and quality-adjusted life years (QALYs) for COPD patients over their lifetimes. Internal validation compared 3 years of predicted cohort experience with ECLIPSE results. Results . At 3 years, the Galaxy COPD model predictions of annual exacerbation rate and annual decline in forced expiratory volume in 1 second fell within the ECLIPSE data confidence limits, although 3-year overall survival was outside the observed confidence limits. Projections of the risk equations over time permitted extrapolation to patient lifetimes. Averaging the predicted cost/QALY outcomes for the different patients within the ECLIPSE cohort gives an estimated lifetime cost of £25,214 (undiscounted)/£20,318 (discounted) and lifetime QALYs of 6.45 (undiscounted/5.24 [discounted]) per ECLIPSE patient. Conclusions . A new form of model for COPD was conceptualized, implemented, and internally validated, based on a series of linked equations using epidemiological data (ECLIPSE) and cost data (TORCH). This Galaxy model predicts COPD outcomes from treatment effects on disease attributes such as lung function, exacerbations, symptoms, or exercise capacity; further external validation is required.

Suggested Citation

  • Andrew H. Briggs & Timothy Baker & Nancy A. Risebrough & Mike Chambers & Sebastian Gonzalez-McQuire & Afisi S. Ismaila & Alex Exuzides & Chris Colby & Maggie Tabberer & Hana Muellerova & Nicholas Loca, 2017. "Development of the Galaxy Chronic Obstructive Pulmonary Disease (COPD) Model Using Data from ECLIPSE: Internal Validation of a Linked-Equations Cohort Model," Medical Decision Making, , vol. 37(4), pages 469-480, May.
  • Handle: RePEc:sae:medema:v:37:y:2017:i:4:p:469-480
    DOI: 10.1177/0272989X16653118
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/0272989X16653118
    Download Restriction: no

    File URL: https://libkey.io/10.1177/0272989X16653118?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Elizabeth G Bond & Lusine Abrahamyan & Mohammad K A Khan & Andrea Gershon & Murray Krahn & Ping Li & Rajibul Mian & Nicholas Mitsakakis & Mohsen Sadatsafavi & Teresa To & Petros Pechlivanoglou & for t, 2020. "Understanding resource utilization and mortality in COPD to support policy making: A microsimulation study," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-16, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Chiranjeev Sanyal & Don Husereau, 2020. "Systematic Review of Economic Evaluations of Services Provided by Community Pharmacists," Applied Health Economics and Health Policy, Springer, vol. 18(3), pages 375-392, June.
    2. Mark Oppe & Daniela Ortín-Sulbarán & Carlos Vila Silván & Anabel Estévez-Carrillo & Juan M. Ramos-Goñi, 2021. "Cost-effectiveness of adding Sativex® spray to spasticity care in Belgium: using bootstrapping instead of Monte Carlo simulation for probabilistic sensitivity analyses," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 22(5), pages 711-721, July.
    3. Kaitlyn Hastings & Clara Marquina & Jedidiah Morton & Dina Abushanab & Danielle Berkovic & Stella Talic & Ella Zomer & Danny Liew & Zanfina Ademi, 2022. "Projected New-Onset Cardiovascular Disease by Socioeconomic Group in Australia," PharmacoEconomics, Springer, vol. 40(4), pages 449-460, April.
    4. Andrea Marcellusi & Raffaella Viti & Loreta A. Kondili & Stefano Rosato & Stefano Vella & Francesco Saverio Mennini, 2019. "Economic Consequences of Investing in Anti-HCV Antiviral Treatment from the Italian NHS Perspective: A Real-World-Based Analysis of PITER Data," PharmacoEconomics, Springer, vol. 37(2), pages 255-266, February.
    5. Risha Gidwani & Louise B. Russell, 2020. "Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers," PharmacoEconomics, Springer, vol. 38(11), pages 1153-1164, November.
    6. Joseph F. Levy & Marjorie A. Rosenberg, 2019. "A Latent Class Approach to Modeling Trajectories of Health Care Cost in Pediatric Cystic Fibrosis," Medical Decision Making, , vol. 39(5), pages 593-604, July.
    7. Qi Cao & Erik Buskens & Hans L. Hillege & Tiny Jaarsma & Maarten Postma & Douwe Postmus, 2019. "Stratified treatment recommendation or one-size-fits-all? A health economic insight based on graphical exploration," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(3), pages 475-482, April.
    8. Jorge Luis García & James J. Heckman, 2021. "Early childhood education and life‐cycle health," Health Economics, John Wiley & Sons, Ltd., vol. 30(S1), pages 119-141, November.
    9. Tushar Srivastava & Nicholas R. Latimer & Paul Tappenden, 2021. "Estimation of Transition Probabilities for State-Transition Models: A Review of NICE Appraisals," PharmacoEconomics, Springer, vol. 39(8), pages 869-878, August.
    10. Eleanor Heather & Katherine Payne & Mark Harrison & Deborah Symmons, 2014. "Including Adverse Drug Events in Economic Evaluations of Anti-Tumour Necrosis Factor-α Drugs for Adult Rheumatoid Arthritis: A Systematic Review of Economic Decision Analytic Models," PharmacoEconomics, Springer, vol. 32(2), pages 109-134, February.
    11. Manuel Gomes & Robert Aldridge & Peter Wylie & James Bell & Owen Epstein, 2013. "Cost-Effectiveness Analysis of 3-D Computerized Tomography Colonography Versus Optical Colonoscopy for Imaging Symptomatic Gastroenterology Patients," Applied Health Economics and Health Policy, Springer, vol. 11(2), pages 107-117, April.
    12. Isaac Corro Ramos & Maureen P. M. H. Rutten-van Mölken & Maiwenn J. Al, 2013. "The Role of Value-of-Information Analysis in a Health Care Research Priority Setting," Medical Decision Making, , vol. 33(4), pages 472-489, May.
    13. Wei Fang & Zhenru Wang & Michael B. Giles & Chris H. Jackson & Nicky J. Welton & Christophe Andrieu & Howard Thom, 2022. "Multilevel and Quasi Monte Carlo Methods for the Calculation of the Expected Value of Partial Perfect Information," Medical Decision Making, , vol. 42(2), pages 168-181, February.
    14. Martin Hoyle, 2008. "Future Drug Prices and Cost-Effectiveness Analyses," PharmacoEconomics, Springer, vol. 26(7), pages 589-602, July.
    15. Bauer, Annette & Knapp, Martin & Alvi, Mohsin & Chaudhry, Nasim & Gregoire, Alain & Malik, Abid & Sikander, Siham & Tayyaba, Kiran & Wagas, Ahmed & Husain, Nusrat, 2024. "Economic costs of perinatal depression and anxiety in a lower-middle income country: Pakistan," LSE Research Online Documents on Economics 122650, London School of Economics and Political Science, LSE Library.
    16. Aris Angelis & Huseyin Naci & Allan Hackshaw, 2020. "Recalibrating Health Technology Assessment Methods for Cell and Gene Therapies," PharmacoEconomics, Springer, vol. 38(12), pages 1297-1308, December.
    17. Yasuhiro Hagiwara & Takeru Shiroiwa, 2022. "Estimating Value-Based Price and Quantifying Uncertainty around It in Health Technology Assessment: Frequentist and Bayesian Approaches," Medical Decision Making, , vol. 42(5), pages 672-683, July.
    18. Neily Zakiyah & Antoinette D I van Asselt & Frank Roijmans & Maarten J Postma, 2016. "Economic Evaluation of Family Planning Interventions in Low and Middle Income Countries; A Systematic Review," PLOS ONE, Public Library of Science, vol. 11(12), pages 1-19, December.
    19. Billingsley Kaambwa & Julie Ratcliffe, 2018. "Predicting EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L) Utilities from Older People’s Quality of Life Brief Questionnaire (OPQoL-Brief) Scores," The Patient: Patient-Centered Outcomes Research, Springer;International Academy of Health Preference Research, vol. 11(1), pages 39-54, February.
    20. Billingsley Kaambwa & Gang Chen & Julie Ratcliffe & Angelo Iezzi & Aimee Maxwell & Jeff Richardson, 2017. "Mapping Between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and Five Multi-Attribute Utility Instruments (MAUIs)," PharmacoEconomics, Springer, vol. 35(1), pages 111-124, January.

    More about this item

    Keywords

    COPD; QALY; cost; risk; model;
    All these keywords.

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:medema:v:37:y:2017:i:4:p:469-480. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.