Adaptive platform trials: definition, design, conduct and reporting considerations

An Author Correction to this article was published on 10 September 2019

This article has been updated


Researchers, clinicians, policymakers and patients are increasingly interested in questions about therapeutic interventions that are difficult or costly to answer with traditional, free-standing, parallel-group randomized controlled trials (RCTs). Examples include scenarios in which there is a desire to compare multiple interventions, to generate separate effect estimates across subgroups of patients with distinct but related conditions or clinical features, or to minimize downtime between trials. In response, researchers have proposed new RCT designs such as adaptive platform trials (APTs), which are able to study multiple interventions in a disease or condition in a perpetual manner, with interventions entering and leaving the platform on the basis of a predefined decision algorithm. APTs offer innovations that could reshape clinical trials, and several APTs are now funded in various disease areas. With the aim of facilitating the use of APTs, here we review common features and issues that arise with such trials, and offer recommendations to promote best practices in their design, conduct, oversight and reporting.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.


All prices are NET prices.

Fig. 1: General operational flow of an adaptive platform trial.

Change history

  • 10 September 2019

    An amendment to this paper has been published and can be accessed via a link at the top of the paper.


  1. 1.

    Bothwell, L. E., Greene, J. A., Podolsky, S. H. & Jones, D. S. Assessing the gold standard — lessons from the history of RCTs. N. Engl. J. Med. 374, 2175–2181 (2016).

    CAS  Article  Google Scholar 

  2. 2.

    Woodcock, J. & LaVange, L. M. Master protocols to study multiple therapies, multiple diseases, or both. N. Engl. J. Med. 377, 62–70 (2017).

    CAS  Article  Google Scholar 

  3. 3.

    Berry, S. M., Connor, J. T. & Lewis, R. J. The platform trial: an efficient strategy for evaluating multiple treatments. JAMA. 313, 1619–1620 (2015).

    Article  Google Scholar 

  4. 4.

    Morris, Z. S., Wooding, S. & Grant, J. The answer is 17 years, what is the question: understanding time lags in translational research. J. R. Soc. Med. 104, 510–520 (2011).

    Article  Google Scholar 

  5. 5.

    Institute of Medicine (US) Committee on Quality of Health Care in America. Crossing the Quality Chasm: A New Health System for the 21st Century. (National Academies Press, 2001).

    Google Scholar 

  6. 6.

    Lai, T. L., Lavori, P. W. & Tsang, K. W. Adaptive design of confirmatory trials: advances and challenges. Contemp. Clin. Trials 45, 93–102 (2015).

    Article  Google Scholar 

  7. 7.

    Berry, D. A. Bayesian clinical trials. Nat. Rev. Drug Discov. 5, 27–36 (2006).

    CAS  Article  Google Scholar 

  8. 8.

    Antoniou, M., Jorgensen, A. L. & Kolamunnage-Dona, R. Biomarker-guided adaptive trial designs in phase II and phase III: a methodological review. PLOS ONE 11, e0149803 (2016).

    Article  Google Scholar 

  9. 9.

    Alexander, B. M. et al. Biomarker-based adaptive trials for patients with glioblastoma—lessons from I-SPY 2. Neuro-oncology 15, 972–978 (2013).

    CAS  Article  Google Scholar 

  10. 10.

    Trippa, L. & Alexander, B. M. Bayesian baskets: a novel design for biomarker-based clinical trials. J. Clin. Oncol. 35, 681–687 (2017).

    Article  Google Scholar 

  11. 11.

    Berry, S. M., Reese, C. S. & Larkey, P. D. Bridging different eras in sports. J. Am. Stat. Associ. 94, 16 (1999).

    Article  Google Scholar 

  12. 12.

    Saville, B. R., Connor, J. T., Ayers, G. D. & Alvarez, J. The utility of Bayesian predictive probabilities for interim monitoring of clinical trials. Clin. Trials 11, 485–493 (2014).

    Article  Google Scholar 

  13. 13.

    Alexander, B. M. et al. Individualized Screening Trial of Innovative Glioblastoma Therapy (INSIGhT): a Bayesian adaptive platform trial to develop precision medicines for patients with glioblastoma. JCO Precis. Oncol. https://doi.org/10.1200/PO.18.00071 (2019).

    Article  Google Scholar 

  14. 14.

    Trippa, L. et al. Bayesian adaptive randomized trial design for patients with recurrent glioblastoma. J. Clin. Oncol. 30, 3258–3263 (2012).

    Article  Google Scholar 

  15. 15.

    Hummel, J., Wang, S. & Kirkpatrick, J. Using simulation to optimize adaptive trial designs: applications in learning and confirmatory phase trials. Clin. Invest. 5, 401–413 (2015).

    CAS  Article  Google Scholar 

  16. 16.

    LaVange, L. M. & Sridhara, R. Innovations in breast cancer drug development—next generation oncology trials: statistical considerations in designing master protocols. FDA http://wayback.archive-it.org/7993/20161023010547/http://www.fda.gov/downloads/Drugs/NewsEvents/UCM423368.pdf (2014).

  17. 17.

    US Food and Drug Administration. Adaptive designs for medical device clinical studies. FDA https://www.fda.gov/ucm/groups/fdagov-public/@fdagov-meddev-gen/documents/document/ucm446729.pdf (2016).

  18. 18.

    London, A. J. Learning health systems, clinical equipoise and the ethics of response adaptive randomisation. J. Med. Ethics 44, 409–415 (2018).

    Article  Google Scholar 

  19. 19.

    Dixon, J. R. Jr. The International Conference on Harmonization Good Clinical Practice guideline. Qual. Assur. 6, 65–74 (1998).

    Article  Google Scholar 

  20. 20.

    International Committee of Medical Journal Editors. Recommendations for the conduct, reporting, editing, and publication of scholarly work in medical journals. ICMJE http://www.icmje.org/icmje-recommendations.pdf (2018).

  21. 21.

    CONSORT. CONSORT 2010. CONSORT http://www.consort-statement.org/consort-2010 (2010).

  22. 22.

    Rugo, H. S. et al. Adaptive randomization of veliparib–carboplatin treatment in breast cancer. N. Engl. J. Med. 375, 23–34 (2016).

    CAS  Article  Google Scholar 

  23. 23.

    Park, J. W. et al. Adaptive randomization of neratinib in early breast cancer. N. Engl. J. Med. 375, 11–22 (2016).

    CAS  Article  Google Scholar 

  24. 24.

    Angus, D. C. Fusing randomized trials with big data: the key to self-learning health care systems? JAMA. 314, 767–768 (2015).

    CAS  Article  Google Scholar 

  25. 25.

    Fiore, L. D. & Lavori, P. W. Integrating randomized comparative effectiveness research with patient care. N. Engl. J. Med. 374, 2152–2158 (2016).

    CAS  Article  Google Scholar 

  26. 26.

    Alexander, B. M. & Cloughesy, T. F. Platform trials arrive on time for glioblastoma. Neuro-oncology 20, 723–725 (2018).

    Article  Google Scholar 

  27. 27.

    Stern, A. D. & Mehta, S. Adaptive platform trials: the clinical trial of the future? Harvard Business School https://www.hbs.edu/faculty/Pages/item.aspx?num=53315 (2018).

  28. 28.

    Alexander, B. M. et al. Brain Malignancy Steering Committee clinical trials planning workshop: report from the Targeted Therapies Working Group. Neuro-oncology 17, 180–188 (2015).

    Article  Google Scholar 

  29. 29.

    Das, S. & Lo, A. W. Re-inventing drug development: a case study of the I-SPY 2 breast cancer clinical trials program. Contemp. Clin. Trials 62, 168–174 (2017).

    Article  Google Scholar 

  30. 30.

    Fernandez, J. M., Stein, R. M. & Lo, A. W. Commercializing biomedical research through securitization techniques. Nat. Biotechnol. 30, 964–975 (2012).

    CAS  Article  Google Scholar 

  31. 31.

    Stern, A. D., Alexander, B. M. & Chandra, A. Innovation incentives and biomarkers. Clin. Pharmacol. Ther. 103, 34–36 (2018).

    CAS  Article  Google Scholar 

  32. 32.

    Korn, E. L. & Freidlin, B. Outcome—adaptive randomization: is it useful? J. Clin. Oncol. 29, 771–776 (2011).

    Article  Google Scholar 

  33. 33.

    Trusheim, M. R. et al. PIPELINEs: creating comparable clinical knowledge efficiently by linking trial platforms. Clin. Pharmacol. Ther. 100, 713–729 (2016).

    CAS  Article  Google Scholar 

  34. 34.

    Saville, B. R. & Berry, S. M. Efficiencies of platform clinical trials: a vision of the future. Clin. Trials 13, 358–366 (2016).

    Article  Google Scholar 

  35. 35.

    Steuer, C. E. et al. Innovative clinical trials: the LUNG-MAP study. Clin. Pharmacol. Ther. 97, 488–491 (2015).

    CAS  Article  Google Scholar 

  36. 36.

    National Cancer Institute Cancer Therapy Evaluation Program. NCI-MATCH Trial (Molecular Analysis for Therapy Choice). NIH http://www.cancer.gov/about-cancer/treatment/clinical-trials/nci-supported/nci-match (updated 9 Apr 2019).

  37. 37.

    Lewis, R. J. et al. Rationale and design of an adaptive phase 2b/3 clinical trial of selepressin for adults in septic shock. Selepressin Evaluation Programme for sepsis-induced shock-adaptive clinical trial. Ann. Am. Thorac Soc. 15, 250–257 (2018).

    Article  Google Scholar 

  38. 38.

    Barker, A. D. et al. I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin. Pharmacol. Ther. 86, 97–100 (2009).

    CAS  Article  Google Scholar 

  39. 39.

    Cortazar, P. et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384, 164–172 (2014).

    Article  Google Scholar 

  40. 40.

    The I-SPY Trials. T-DM1 (Kadcyla) and pertuzumab (Perjeta) show promise for women with HER2-positive breast cancer. The I-SPY Trials https://www.ispytrials.org/newsitems/2016-tdm1-pertuzumab-graduation-press-release (2016).

  41. 41.

    The I-SPY Trials. Merck & Co. MK-2206 ‘graduates’ from I-SPY2. The I-SPY Trials https://www.ispytrials.org/newsitems/2015-mk2206-graduation-press-release (2015).

  42. 42.

    Alexander, B. M. et al. Adaptive global innovative learning environment for glioblastoma: GBM AGILE. Clin. Cancer Res. 24, 737–743 (2018).

    Article  Google Scholar 

  43. 43.

    Berry, S. M., Carlin, B. P., Lee, J. J. & Mueller, P. Bayesian Adaptive Methods for Clinical Trials 1st edn (CRC Press, 2010).

  44. 44.

    Thorlund, K., Haggstrom, J., Park, J. J. & Mills, E. J. Key design considerations for adaptive clinical trials: a primer for clinicians. BMJ 360, k698 (2018).

    Article  Google Scholar 

  45. 45.

    Ritchie, C. W. et al. Development of interventions for the secondary prevention of Alzheimer’s dementia: The European Prevention of Alzheimer’s Dementia (EPAD) project. Lancet Psychiatry 3, 179–186 (2016).

    PubMed  Google Scholar 

Download references


The authors are grateful to the Harvard/Massachusetts Institute of Technology (MIT) Center for Regulatory Science for hosting the meeting, and to the Harvard/MIT Center for Regulatory Science, the University of Pittsburgh, the PREPARE consortium, REMAP-CAP and Berry Consultants for financial support towards travel, lodging and expenses. They thank C. D’Avanzo, L. Dilworth, L. Maliszewski (Harvard University), R. Rice (University of Pittsburgh) and J. Clenell (Berry Consultants) for organizational and administrative support and J. Vates (University of Pittsburgh) for detailed review and editing.

Author information



Corresponding authors

Correspondence to Derek C. Angus or Brian M. Alexander.

Ethics declarations

Competing interests

B.M.A. reports employment at Foundation Medicine, Inc.; personal fees from Bristol-Myers Squibb, Schlesinger Associates and Precision Health Economics; and unpaid leadership as president and CEO of the Global Coalition for Adaptive Research, a non-profit research organization. S. Berry reports being a part owner of Berry Consultants, LLC (a company that designs and implements platform and adaptive clinical trials for pharmaceutical companies, medical device companies, National Institutes of Health cooperative groups, international consortia and non-profit organizations) and providing consulting for platform trials. M. Buxton, M. Paoloni and K. Viele are currently employees of Berry Consultants, LLC. R. Lewis is a senior medical scientist at Berry Consultants, LLC. D. A. Berry is a co-owner of Berry Consultants, LLC. B. Hyman has current research support from AbbVie, Amgen, Denali, Fidelity Biosciences, Eli Lilly, Merck and co. and Arvinas; serves as a consultant for Biogen, Ceregeen, Genentech, Roche, Eli Lilly, Neurophage, Novartis, Takeda and Sunovian; and owns stock in, as well as having a family member who works for, Novartis. M. Krams is an employee of Johnson & Johnson. A.W. Lo has personal investments in biotechnology companies, biotech venture capital funds and mutual funds; serves as an adviser to BridgeBio Capital; is director of Roivant Sciences Ltd. and chairman emeritus and senior adviser to AlphaSimplex Group; has during the past 6 years received speaking/consulting fees or honoraria from AIG, AlphaSimplex Group, BIS, BridgeBio Capital, Citigroup, Chicago Mercantile Exchange, Financial Times, Harvard University, IMF, National Bank of Belgium, Q Group, Roivant Sciences, Scotia Bank, State Street Bank, University of Chicago and Yale University; and is a director of the Massachusetts Institute of Technology Whitehead Institute for Biomedical Research and a member of the Board of Overseers of Beth Israel Deaconess Medical Center. C. Ritchie provides consultancy and/or receives grant funding from Actinogen, Allergan, Biogen, Eisai, Alector, Janssen, MSD, Lundbeck, Prana Biotechnology, AbbVie, Roche, Eli Lilly and Pfizer. B. Spellberg in the last 12 months consulted for Bayer, Forge, Shionogi, Alexion, Synthetic Biologics, Paratek, TheoremDx, Bioversys and Acurx; and owns equity in Motif, BioAIM, Synthetic Biologics, Mycomed and ExBaq. M. Trusheim is owner of Co-Bio Consulting LLC, which provides services to biomedical companies, and has received speaking fees over the past 6 years from Cowen Group, Merck & Co. and Shire. P. Y. Wen reports receiving research support from Eli Lilly, Puma and Celgene and serving on advisory boards for Eli Lilly and Puma. The remaining authors declare no competing interests.

Additional information


Views expressed here are the authors’ and do not necessarily reflect the position of the US Food and Drug Administration.

Publisher’s note

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

Related links

ClinicalTrials.gov: www.clinicaltrials.gov

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

The Adaptive Platform Trials Coalition., Angus, D.C., Alexander, B.M. et al. Adaptive platform trials: definition, design, conduct and reporting considerations. Nat Rev Drug Discov 18, 797–807 (2019). https://doi.org/10.1038/s41573-019-0034-3

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing