Skip to main content

Advertisement

SpringerLink
Log in

Real-World Data as External Controls: Practical Experience from Notable Marketing Applications of New Therapies

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

Abstract

Introduction

Real-world data (RWD) can contextualize findings from single-arm trials when randomized comparative trials are unethical or unfeasible. Findings from single-arm trials alone are difficult to interpret and a comparison, when feasible and meaningful, to patient-level information from RWD facilitates the evaluation. As such, there have been several recent regulatory applications including RWD or other external data to support the product’s efficacy and safety. This paper summarizes some lessons learned from such contextualization from 20 notable new drug or biologic licensing applications in oncology and rare diseases.

Methods

This review focuses on 20 notable new drug or biologic licensing applications that included patient-level RWD or other external data for contextualization of trial results. Publicly available regulatory documents including clinical and statistical reviews, advisory committee briefing materials and minutes, and approved product labeling were retrieved for each application. The authors conducted independent assessments of these documents focusing on the regulatory evaluation, in each case. Three examples are presented in detail to illustrate the salient issues and themes identified across applications.

Results

Regulatory decisions were strongly influenced by the quality and usability of the RWD. Comparability of cohort attributes such as endpoints, populations, follow-up, index and censoring criteria, as well as data completeness and accuracy of key variables appeared to be essential to ensure the quality and relevance of the RWD. Given adequate sample size of the clinical trials or external control, the use of appropriate analytic methods to properly account for confounding, such as regression or matching, and pre-specification of these methods while blinded to patient outcomes seemed good strategies to address baseline differences.

Discussion

Contextualizing single-arm trials with patient-level RWD appears to be an advance in regulatory science; however, challenges remain. Statisticians and epidemiologists have long focused on analytical methods for comparative effectiveness but hurdles in use of RWD have often occurred upstream of the analyses. More specifically, we noted hurdles in evaluating data quality, justifying cohort selection or initiation of follow-up, and demonstrating comparability of cohorts and endpoints.

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

Similar content being viewed by others

References

  1. Guidance for Industry E10 Choice of Control Group and Related Issues in Clinical Trials. FDA guidance document. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/e10-choice-control-group-and-related-issues-clinical-trials (2001). Accessed 25 June 2021

  2. Guidance for Industry Rare Diseases: Natural History Studies for Drug Development. FDA guidance document. https://www.fda.gov/regulatory-information/search-fda-guidance-documents/rare-diseases-natural-history-studies-drug-development (2019) Accessed 25 June 2021

  3. Levenson MS. Regulatory-grade clinical trial design using real-world data. Clin Trials. 2020;17(4):377–82. https://doi.org/10.1177/1740774520905576.

    Article  PubMed  PubMed Central  Google Scholar 

  4. 21st Century Cures Act. H.R. 24, 114th Congress (2016). https://www.congress.gov/114/bills/hr34/BILLS-114hr34enr.pdf. Accessed 04 Sept 2020.

  5. Real World Evidence, FDA. https://www.fda.gov/science-research/science-and-research-special-topics/real-world-evidence (2020). Accessed 25 June 2021

  6. Guidance for Industry Real-World Data: Assessing Electronic Health Records and Medical Claims Data to Support Regulatory Decision-Making for Drug and Biological Products. FDA Guidance Document 2021, https://www.fda.gov/media/152503/download (2021). Accessed 30 Sept 2021

  7. Guidance for Industry Data Standards for Drug and Biological Product Submissions containing Real-World Data. FDA Guidance Document 2021, https://www.fda.gov/regulatory-information/search-fda-guidance-documents/data-standards-drug-and-biological-product-submissions-containing-real-world-data (2021). Accessed 24 Oct 2021

  8. Guidance for Industry Real-World Data: Assessing Registries to Support Regulatory Decision-Making for Drug and Biological Products. https://www.fda.gov/media/154449/download (2021) Accessed 21 Dec 2021

  9. Seifu Y, Gamalo-Siebers M, Barthel FM, et al. Real-world evidence utilization in clinical development reflected by US product labeling: statistical review. Ther Innov Regul Sci. 2020;54(6):1436–43. https://doi.org/10.1007/s43441-020-00170-y.

    Article  PubMed  Google Scholar 

  10. FDA Center for Drug Evaluation and Research. BALVERSA (erdafitinib) Application number: NDA 212018. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212018Orig1s000TOC.cfm (2019). Accessed 04 Sept 2020

  11. FDA Center for Drug Evaluation and Research. XPOVIO® (selinexor) Application number: NDA 212306. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/212306Orig1s000TOC.cfm (2019). Accessed 30 Oct 2020

  12. FDA Center for Drug Evaluation and Research. BRINEURA (cerliponase alfa) Application number: BLA 761052. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2017/761052Orig1s000TOC.cfm (2017). Accessed 30 Oct 2020

  13. FDA Center for Drug Evaluation and Research. myozyme (alglucosidase alfa) Application number: BLA 125141. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2006/125141s000_MyozymeTOC.cfm (2006). Accessed 30 Oct 2020

  14. FDA Center for Biologics Evaluation and Research. Antithrombin (recombinant) Application number: BLA 125284. https://www.fda.gov/vaccines-blood-biologics/approved-blood-products/atryn (2009). Accessed 31 Jan 2021

  15. FDA Center for Biologics Evaluation and Research. Hepatitis B immune globulin intravenous (Human) Application number: BLA 125035. https://wayback.archive-it.org/7993/20170404205345/https:/www.fda.gov/BiologicsBloodVaccines/BloodBloodProducts/ApprovedProducts/LicensedProductsBLAs/FractionatedPlasmaProducts/ucm089380.htm (2012). Accessed 31 Jan 2021

  16. FDA Center for Drug Evaluation and Research. cholbam (cholic acid) Application number: NDA 205750. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/205750Orig1s000TOC.cfm (2015). Accessed 31 Jan 2021

  17. FDA Center for Drug Evaluation and Research. strensiq (asfotase alfa) Application number: BLA 125513. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/125513Orig1s000SumR.pdf (2015). Accessed 30 Oct 2020

  18. FDA Center for Drug Evaluation and Research. VISTOGARD (uridine triacetate)Application number: NDA 208159. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2015/208159Orig1s000TOC.cfm (2015). Accessed 31 Jan 2021

  19. FDA Center for Drug Evaluation and Research. defitelio (defibrotide sodium) Application number: NDA 208114. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2016/208114Orig1s000TOC.cfm (2016). Accessed 30 Oct 2020

  20. FDA Center for Drug Evaluation and Research. TEPADINA (thiotepa) Application number: NDA 208264. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2017/208264Orig1s000TOC.cfm (2017). Accessed 31 Jan 2021

  21. FDA Center for Drug Evaluation and Research. BAVENCIO (avelumab) Application number: BLA 761049. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2017/761049Orig1s000TOC.cfm (2017). Accessed 24 Oct 2020

  22. FDA Center for Biologics Evaluation and Research. YESCARTA (axicabtagene ciloleucel) Application number: BLA 125643. https://www.fda.gov/vaccines-blood-biologics/cellular-gene-therapy-products/yescarta-axicabtagene-ciloleucel (2017). Accessed 04 Sept 2020

  23. FDA Center for Drug Evaluation and Research. LUTATHERA (lutetium lu 177 dotate) Application number: NDA 208700. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2018/208700Orig1s000TOC.cfm (2018). Accessed 31 Jan 2021

  24. FDA Center for Drug Evaluation and Research. OMEGAVEN (fish oil triglycerides) Application number: NDA 210589. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2018/210589Orig1s000TOC.cfm (2017). Accessed 25 Oct 2020

  25. FDA Center for Drug Evaluation and Research. BLINCYTO (blinatumomab) Application number: BLA 125557. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2018/125557Orig1s013.pdf (2018). Accessed 04 Sept 2020

  26. FDA Center for Drug Evaluation and Research. IBRANCE (palbociclib capsule) Application number: NDA 207103. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2019/207103Orig1s008.pdf (2019). Accessed 04 Sept 2020

  27. FDA Center for Biologics Evaluation and Research. ZOLGENSMA (onasemnogene abeparvovec-xioi) Application number: BLA 125596. https://www.fda.gov/vaccines-blood-biologics/zolgensma (2019). Accessed 31 Jan 2021

  28. FDA Center for Drug Evaluation and Research. DOLJOVI (triheptanoin) Application number: NDA 213687. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2020/213687Orig1s000TOC.cfm (2020). Accessed 14 Dec 2020

  29. FDA Center for Drug Evaluation and Research. MONJUVI (tafasitamab-cxix) Application number: BLA 761163. https://www.accessdata.fda.gov/drugsatfda_docs/nda/2020/761163Orig1s000TOC.cfm (2020). Accessed 14 Dec 2020

  30. Eisenhauer E, Therasse P, Bogaerts J, et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur J Cancer. 2009;45:228.

    Article  CAS  Google Scholar 

  31. Gillingham MB, Heitner SB, Martin M, et al. Triheptanoin versus trioctanoin for long-chain fatty acid oxidation disorders: a double blinded, randomized controlled trial. J Inherit Metab Dis. 2021;40(6):831–43.

    Article  Google Scholar 

  32. Burger HU, Gerlinger C, Harbron C, et al. The use of external controls: to what extent can it currently be recommended? Pharm Stat. 2021. https://doi.org/10.1002/pst.2120.

    Article  PubMed  Google Scholar 

  33. Ghadessi M, Tang R, Zhou J, et al. A roadmap to using historical controls in clinical trials—by drug information association adaptive design scientific working group (DIA-ADSWG). Orphanet J Rare Dis. 2020;15:69.

    Article  Google Scholar 

  34. Lim J, Walley R, Yuan J, et al. Minimizing patient burden through the use of historical subject-level data in innovative confirmatory clinical trials: review of methods and opportunities. Ther Innov Regul Sci. 2018;52(5):546–59. https://doi.org/10.1177/2168479018778282.

    Article  PubMed  Google Scholar 

  35. Lu N, Xu Y, Yue LQ. Some considerations on design and analysis plan on a nonrandomized comparative study using propensity score methodology for medical device premarket evaluation. Stat Biopharm Res. 2020;12(2):155–63. https://doi.org/10.1080/19466315.2019.1647873.

    Article  Google Scholar 

  36. Bertagnolli MM, Anderson B, Quina A, et al. The electronic health record as a clinical trials tool: opportunities and challenges. Clin Trials. 2020;17(3):237–42. https://doi.org/10.1177/1740774520913819.

    Article  PubMed  Google Scholar 

  37. Collins R, Bowman L, Landray M, Peto R. The magic of randomization versus the myth of real-world evidence. N Engl J Med. 2020;382(7):674–8. https://doi.org/10.1056/NEJMsb1901642.

    Article  PubMed  Google Scholar 

  38. US Food and Drug Administration. FDA approves new use of transplant drug based on real-world evidence [Press Release]. https://www.fda.gov/drugs/news-events-human-drugs/fda-approves-new-use-transplant-drug-based-real-world-evidence (2021). Accessed 19 July 2021.

Download references

Funding

No funding support was received for the research, authorship, and/or publication of this article.

Author information

Authors and Affiliations

  1. Statistical Methodology and Consulting, Novartis, Basel, Switzerland

    Rima Izem

  2. Biometrics, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, 101 Orchard Ridge Dr., Gaithersburg, MD, 20878, USA

    Joan Buenconsejo

  3. Data Science, Acorn AI, Medidata, New York, NY, USA

    Ruthanna Davi

  4. Regulatory Affairs, Cardiovascular, Renal and Metabolism, BioPharmaceuticals R&D, AstraZeneca, Gaithersburg, MD, USA

    Jingyu Julia Luan

  5. Medical and RWD Analytics, Otsuka Pharmaceutical Development and Commercialization, Inc., Princeton, NJ, USA

    LaRee Tracy

  6. Global Biometrics and Data Management, Inflammation and Immunology Statistics, Pfizer, Collegeville, PA, USA

    Margaret Gamalo

Authors
  1. Rima Izem
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joan Buenconsejo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ruthanna Davi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jingyu Julia Luan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. LaRee Tracy
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Margaret Gamalo
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

RI is an employee of Novartis. JB contributed to this work while she was an employee of AstraZeneca. She is now an employee and a stockholder of Bristol Myers Squibb. RD is an employee and a stockholder of Medidata, Dassault Systemes. JL is an employee and a stockholder of AstraZeneca. LT contributed to this work while she was an employee of Otsuka. She is now an employee and a stockholder of Moderna. MG is an employee and stockholder of Pfizer.

Corresponding author

Correspondence to Joan Buenconsejo.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Izem, R., Buenconsejo, J., Davi, R. et al. Real-World Data as External Controls: Practical Experience from Notable Marketing Applications of New Therapies. Ther Innov Regul Sci 56, 704–716 (2022). https://doi.org/10.1007/s43441-022-00413-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s43441-022-00413-0

Keywords

Search

Navigation