IDEAS home Printed from
   My bibliography  Save this article

Surrogate Endpoints in Health Technology Assessment: An International Review of Methodological Guidelines


  • Bogdan Grigore

    (Institute of Health Research, University of Exeter)

  • Oriana Ciani

    (Institute of Health Research, University of Exeter
    SDA Bocconi)

  • Florian Dams

    (University of Bern)

  • Carlo Federici

    (SDA Bocconi)

  • Saskia Groot

    (Erasmus University Rotterdam)

  • Meilin Möllenkamp

    (Universität Hamburg)

  • Stefan Rabbe

    (Universität Hamburg)

  • Kosta Shatrov

    (University of Bern)

  • Antal Zemplenyi

    (Syreon Research Institute
    University of Pécs)

  • Rod S. Taylor

    (Institute of Health Research, University of Exeter
    University of Glasgow)


In the drive towards faster patient access to treatments, health technology assessment (HTA) agencies are increasingly faced with reliance on evidence from surrogate endpoints, leading to increased decision uncertainty. This study undertook an updated survey of methodological guidance for using surrogate endpoints across international HTA agencies. We reviewed HTA and economic evaluation methods guidance from European, Australian and Canadian HTA agencies. We considered how guidelines addressed the methods for handling surrogate endpoints, including (1) level of evidence, (2) methods of validation, and (3) thresholds of acceptability. Across the 73 HTA agencies surveyed, 29 (40%) had methodological guidelines that made specific reference to consideration of surrogate outcomes. Of the 45 methods documents analysed, the majority [27 (60%)] were non-technology specific, 15 (33%) focused on pharmaceuticals and three (7%) on medical devices. The principles of the European network for Health Technology Assessment (EUnetHTA) guidelines published in 2015 on the handling of surrogate endpoints appear to have been adopted by many European HTA agencies, i.e. preference for final patient-relevant outcomes and reliance on surrogate endpoints with biological plausibility and epidemiological evidence of the association between the surrogate and final endpoint. Only a small number of HTA agencies (UK National Institute for Care and Excellence; the German Institute for Medical Documentation and Information and Institute for Quality and Efficiency in Health Care; the Australian Pharmaceutical Benefits Advisory Committee; and the Canadian Agency for Drugs and Technologies in Health) have developed more detailed prescriptive criteria for the acceptance of surrogate endpoints, e.g. meta-analyses of randomised controlled trials showing strong association between the treatment effect on the surrogate and final outcomes. As the decision uncertainty associated with reliance on surrogate endpoints carries a risk to patients and society, there is a need for HTA agencies to develop more detailed methodological guidance for consistent selection and evaluation of health technologies that lack definitive final patient-relevant outcome evidence at the time of the assessment.

Suggested Citation

  • Bogdan Grigore & Oriana Ciani & Florian Dams & Carlo Federici & Saskia Groot & Meilin Möllenkamp & Stefan Rabbe & Kosta Shatrov & Antal Zemplenyi & Rod S. Taylor, 2020. "Surrogate Endpoints in Health Technology Assessment: An International Review of Methodological Guidelines," PharmacoEconomics, Springer, vol. 38(10), pages 1055-1070, October.
  • Handle: RePEc:spr:pharme:v:38:y:2020:i:10:d:10.1007_s40273-020-00935-1
    DOI: 10.1007/s40273-020-00935-1

    Download full text from publisher

    File URL:
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL:
    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

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    1. Ruof, Jörg & Knoerzer, Dietrich & Dünne, Anja-Alexandra & Dintsios, Charalabos-Markos & Staab, Thomas & Schwartz, Friedrich Wilhelm, 2014. "Analysis of endpoints used in marketing authorisations versus value assessments of oncology medicines in Germany," Health Policy, Elsevier, vol. 118(2), pages 242-254.
    2. Karl Claxton & Mark Sculpher & Chris McCabe & Andrew Briggs & Ron Akehurst & Martin Buxton & John Brazier & Tony O'Hagan, 2005. "Probabilistic sensitivity analysis for NICE technology assessment: not an optional extra," Health Economics, John Wiley & Sons, Ltd., vol. 14(4), pages 339-347, April.
    Full references (including those not matched with items on IDEAS)


    Blog mentions

    As found by, the blog aggregator for Economics research:
    1. Chris Sampson’s journal round-up for 12th October 2020
      by Chris Sampson in The Academic Health Economists' Blog on 2020-10-12 11:00:03

    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. Bogdan Grigore & Oriana Ciani & Florian Dams & Carlo Federici & Saskia Groot & Meilin Möllenkamp & Stefan Rabbe & Kosta Shatrov & Antal Zemplenyi & Rod S. Taylor, 0. "Surrogate Endpoints in Health Technology Assessment: An International Review of Methodological Guidelines," PharmacoEconomics, Springer, vol. 0, pages 1-16.
    2. A. E. Ades & Karl Claxton & Mark Sculpher, 2006. "Evidence synthesis, parameter correlation and probabilistic sensitivity analysis," Health Economics, John Wiley & Sons, Ltd., vol. 15(4), pages 373-381, April.
    3. Dongzhe Hong & Lei Si & Minghuan Jiang & Hui Shao & Wai-kit Ming & Yingnan Zhao & Yan Li & Lizheng Shi, 2019. "Cost Effectiveness of Sodium-Glucose Cotransporter-2 (SGLT2) Inhibitors, Glucagon-Like Peptide-1 (GLP-1) Receptor Agonists, and Dipeptidyl Peptidase-4 (DPP-4) Inhibitors: A Systematic Review," PharmacoEconomics, Springer, vol. 37(6), pages 777-818, June.
    4. Pedram Sendi & Huldrych F Günthard & Mathew Simcock & Bruno Ledergerber & Jörg Schüpbach & Manuel Battegay & for the Swiss HIV Cohort Study, 2007. "Cost-Effectiveness of Genotypic Antiretroviral Resistance Testing in HIV-Infected Patients with Treatment Failure," PLOS ONE, Public Library of Science, vol. 2(1), pages 1-8, January.
    5. 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.
    6. McKenna, Claire & Chalabi, Zaid & Epstein, David & Claxton, Karl, 2010. "Budgetary policies and available actions: A generalisation of decision rules for allocation and research decisions," Journal of Health Economics, Elsevier, vol. 29(1), pages 170-181, January.
    7. Mattias Ekman & Peter Lindgren & Carolin Miltenburger & Genevieve Meier & Julie Locklear & Mary Chatterton, 2012. "Cost Effectiveness of Quetiapine in Patients with Acute Bipolar Depression and in Maintenance Treatment after an Acute Depressive Episode," PharmacoEconomics, Springer, vol. 30(6), pages 513-530, June.
    8. Emma McIntosh, 2006. "Using Discrete Choice Experiments within a Cost-Benefit Analysis Framework," PharmacoEconomics, Springer, vol. 24(9), pages 855-868, September.
    9. John Hutton, 2012. "‘Health Economics’ and the evolution of economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 21(1), pages 13-18, January.
    10. Sofia Dias & Alex J. Sutton & Nicky J. Welton & A. E. Ades, 2013. "Evidence Synthesis for Decision Making 6," Medical Decision Making, , vol. 33(5), pages 671-678, July.
    11. Alan Brennan & Stephen E. Chick & Ruth Davies, 2006. "A taxonomy of model structures for economic evaluation of health technologies," Health Economics, John Wiley & Sons, Ltd., vol. 15(12), pages 1295-1310, December.
    12. C. M. Dintsios & I. Beinhauer, 2020. "The impact of additive or substitutive clinical study design on the negotiated reimbursement for oncology pharmaceuticals after early benefit assessment in Germany," Health Economics Review, Springer, vol. 10(1), pages 1-25, December.
    13. Laura Bojke & Karl Claxton & Stephen Palmer & Mark Sculpher, 2006. "Defining and characterising structural uncertainty in decision analytic models," Working Papers 009cherp, Centre for Health Economics, University of York.
    14. Sun-Young Kim & Louise B. Russell & Anushua Sinha, 2015. "Handling Parameter Uncertainty in Cost-Effectiveness Models Simply and Responsibly," Medical Decision Making, , vol. 35(5), pages 567-569, July.
    15. Nicky J. Welton & Jason J. Madan & Deborah M. Caldwell & Tim J. Peters & Anthony E. Ades, 2014. "Expected Value of Sample Information for Multi-Arm Cluster Randomized Trials with Binary Outcomes," Medical Decision Making, , vol. 34(3), pages 352-365, April.
    16. Nicholas Graves & Mary Courtney & Helen Edwards & Anne Chang & Anthony Parker & Kathleen Finlayson, 2009. "Cost-Effectiveness of an Intervention to Reduce Emergency Re-Admissions to Hospital among Older Patients," PLOS ONE, Public Library of Science, vol. 4(10), pages 1-9, October.
    17. Anthony Newall & Mark Jit & Philippe Beutels, 2012. "Economic Evaluations of Childhood Influenza Vaccination," PharmacoEconomics, Springer, vol. 30(8), pages 647-660, August.
    18. Christopher McCabe & Giovanni Tramonti & Andrew Sutton & Peter Hall & Mike Paulden, 2021. "Probabilistic One-Way Sensitivity Analysis with Multiple Comparators: The Conditional Net Benefit Frontier," PharmacoEconomics, Springer, vol. 39(1), pages 19-24, January.
    19. Nicholas Graves & Katie Page & Elizabeth Martin & David Brain & Lisa Hall & Megan Campbell & Naomi Fulop & Nerina Jimmeison & Katherine White & David Paterson & Adrian G Barnett, 2016. "Cost-Effectiveness of a National Initiative to Improve Hand Hygiene Compliance Using the Outcome of Healthcare Associated Staphylococcus aureus Bacteraemia," PLOS ONE, Public Library of Science, vol. 11(2), pages 1-17, February.
    20. Anthony O'Hagan & Matt Stevenson & Jason Madan, 2007. "Monte Carlo probabilistic sensitivity analysis for patient level simulation models: efficient estimation of mean and variance using ANOVA," Health Economics, John Wiley & Sons, Ltd., vol. 16(10), pages 1009-1023, October.

    More about this item


    Access and download statistics


    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:spr:pharme:v:38:y:2020:i:10:d:10.1007_s40273-020-00935-1. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: .

    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: Sonal Shukla or Springer Nature Abstracting and Indexing (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 hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.