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Pragmatic Trials and Approaches to Transforming Care

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Clinical Trials

Part of the book series: Success in Academic Surgery ((SIAS))

Abstract

When considering medical interventions and tools for learning and advancing a field, one perspective is to test a drug, device, or intervention in a highly controlled setting, where inclusion and exclusion criteria are very strict. Such criteria can limit the impact of bias and confounders, while providing rigor in assessing the impact of an intervention. However, when evaluating data from a clinical trial and assessing whether these criteria apply to the patient who sits in front of you, this approach creates a challenge: such strict criteria often mean that the person for whom you want to apply “the evidence” is not appropriate. For this reason, there has been a move to conduct more pragmatic trials that are designed to test the effectiveness of the intervention in broad routine clinical practice. Often, interventions that show a dramatic impact in the setting of a clinical trial fail to be effective in broader settings. This phenomenon is called regression to the mean [1]. So one way to try to approach the assessment of drug, device, and surgical interventions is to evaluate them using a pragmatic trial approach.This establishes a broader base for the intervention and, prospectively, you can identify the subgroups where the interventions could be found to be more effective. This improves applicability of the results.

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References

  1. Barnett AG, van der Pols JC, Dobson AJ. Regression to the mean: what it is and how to deal with it. Int J Epidemiol. 2004;34(1):215–20. https://doi.org/10.1093/ije/dyh299.

    Article  PubMed  Google Scholar 

  2. Schwartz D, Lellouch J. Explanatory and pragmatic attitudes in therapeutical trials. J Clin Epidemiol. 2009;62(5):499–505. https://doi.org/10.1016/j.jclinepi.2009.01.012.

    Article  PubMed  Google Scholar 

  3. Ford I, Norrie J. Pragmatic trials. N Engl J Med. 2016;375(5):454–63. https://doi.org/10.1056/NEJMra1510059.

    Article  PubMed  Google Scholar 

  4. Roland ME, Barin B, Huprikar S, et al. Survival in HIV-positive transplant recipients compared with transplant candidates and with HIV-negative controls. AIDS. 2016;30(3):435–44. https://doi.org/10.1097/QAD.0000000000000934.

    Article  PubMed  PubMed Central  Google Scholar 

  5. Stock PG, Barin B, Murphy B, et al. Outcomes of kidney transplantation in HIV-infected recipients. N Engl J Med. 2010;363(21):2004–14. https://doi.org/10.1056/NEJMoa1001197.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Coffin CS, Stock PG, Dove LM, et al. Virologic and clinical outcomes of hepatitis B virus infection in HIV-HBV coinfected transplant recipients. Am J Transplant. 2010;10(5):1268–75. https://doi.org/10.1111/j.1600-6143.2010.03070.x.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Terrault NA, Roland ME, Schiano T, et al. Outcomes of liver transplant recipients with hepatitis C and human immunodeficiency virus coinfection. Liver Transpl. 2012;18(6):716–26. https://doi.org/10.1002/lt.23411.

    Article  PubMed  PubMed Central  Google Scholar 

  8. Esserman LJ, Thompson IM, Reid B. Overdiagnosis and overtreatment in cancer. JAMA. 2013;310(8):797–8. https://doi.org/10.1001/jama.2013.108415.

    Article  CAS  PubMed  Google Scholar 

  9. Esserman LJ. The WISDOM study: breaking the deadlock in the breast cancer screening debate. npj Breast Cancer. 2017;3(1):34. https://doi.org/10.1038/s41523-017-0035-5.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Blue Cross Blue Shield Association. Blue cross and blue shield companies seek to personalize breast cancer screening through the WISDOM study. bcbs.com. https://www.bcbs.com/press-releases/blue-cross-and-blue-shield-companies-seek-personalize-breast-cancer-screening. Published 2 May 2018. Accessed 1 July 2019.

  11. Rosenberg-Wohl S, Thygeson M, Stover Fiscalini A, et al. Private payer participation in coverage with evidence development: a case study. In: Health affairs. http://healthaffairs.org/blog/2017/03/14/private-payer-participation-in-coverage-with-evidence-development-a-case-study/. Published March 14, 2017. Accessed 4 Apr 2017.

  12. eCQI Resource Center. Breast cancer screening eCQMs for 2018 performance period. ecqi.healthit.gov. https://ecqi.healthit.gov/ecqm/ep/2018/cms125v6. Published July 22, 2019. Accessed 10 Aug 2019.

  13. United States Renal Data System. 2018 USRDS annual data report: epidemiology of kidney disease in the United States. usrds.org. https://www.usrds.org/adr.aspx. Published 2018. Accessed 1 Aug 2019.

  14. Wong G, Webster AC. Cancer after kidney transplantation. In: Chapman JR, editor. Oxford textbook of clinical nephrology. Vol 1. Cancer after kidney transplantation. Oxford: Oxford University Press. https://doi.org/10.1093/med/9780199592548.003.0287.

  15. Penn I. Cancers in renal transplant recipients. Adv Ren Replace Ther. 2000;7(2):147–56.

    Article  CAS  Google Scholar 

  16. Knoll G, Cockfield S, Blydt-Hansen T, et al. Canadian Society of Transplantation: consensus guidelines on eligibility for kidney transplantation. CMAJ. 2005;173:S1–25. https://doi.org/10.1503/cmaj.1041588.

    Article  PubMed  PubMed Central  Google Scholar 

  17. Mukhtar RA, Piper ML, Freise C, Veer LJV, Baehner FL, Esserman LJ. The novel application of genomic profiling assays to shorten inactive status for potential kidney transplant recipients with breast cancer. Am J Transplant. 2017;17(1):292–5. https://doi.org/10.1111/ajt.14003.

    Article  CAS  PubMed  Google Scholar 

  18. Wolf DM, Yau C, Wulfkuhle JD, et al. Integration of DNA repair deficiency and immune biomarkers to predict which early-stage triple-negative breast cancer patients are likely to respond to platinum-containing regimens vs. immunotherapy: the neoadjuvant I-SPY 2 trial. Chicago, IL; 2019.

    Google Scholar 

  19. Esserman LJ, Moore DH, Tsing PJ, et al. Biologic markers determine both the risk and the timing of recurrence in breast cancer. Breast Cancer Res Treat. 2011;129(2):607–16. https://doi.org/10.1007/s10549-011-1564-5.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Esserman LJ, Yau C, Thompson CK, et al. Use of molecular tools to identify patients with indolent breast cancers with ultralow risk over 2 decades. JAMA Oncol. 2017;3(11):1503–10. https://doi.org/10.1001/jamaoncol.2017.1261.

    Article  PubMed  PubMed Central  Google Scholar 

  21. Mahajan R, Gupta K. Adaptive design clinical trials: methodology, challenges and prospect. Indian J Pharmacol. 2010;42(4):201–7. https://doi.org/10.4103/0253-7613.68417.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Woodcock J, LaVange LM. Master protocols to study multiple therapies, multiple diseases, or both. N Engl J Med. 2017;377(1):62–70. https://doi.org/10.1056/NEJMra1510062.

    Article  CAS  PubMed  Google Scholar 

  23. Sparano JA, Gray RJ, Makower DF, et al. Adjuvant chemotherapy guided by a 21-gene expression assay in breast cancer. N Engl J Med. 2018;379(2):111–21. https://doi.org/10.1056/NEJMoa1804710.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Symmans WF, Peintinger F, Hatzis C, et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J Clin Oncol. 2007;25(28):4414–22. https://doi.org/10.1200/JCO.2007.10.6823.

    Article  PubMed  Google Scholar 

  25. Yee D, DeMichele A, Isaacs C, et al. Pathological complete response predicts event-free and distant disease-free survival in the I-SPY2 TRIAL (Abstract GS3-08). Cancer Res. 2018;78(4 Suppl) https://doi.org/10.1158/1538-7445.SABCS17-GS3-08.

  26. Woodcock J. Precompetitive research: a new prescription for drug development? Clin Pharmacol Ther. 2010:521–3.

    Google Scholar 

  27. Barker AD, Sigman CC, Kelloff GJ, Hylton NM, Berry DA, Esserman LJ. I-SPY 2: an adaptive breast cancer trial design in the setting of neoadjuvant chemotherapy. Clin Pharmacol Ther. 2009;86(1):97–100. https://doi.org/10.1038/clpt.2009.68.

    Article  CAS  PubMed  Google Scholar 

  28. Esserman LJ, Barker AD, Woodcock J, et al. A model for accelerating identification and regulatory approval of effective investigational agents. Cureus. 2012;4(12):e76. https://doi.org/10.7759/cureus.76.

    Article  Google Scholar 

  29. Nanda R, Liu MC, Yau C, et al. Pembrolizumab plus standard neoadjuvant therapy for high-risk breast cancer (BC): results from I-SPY 2 (Abstract 506). J Clin Oncol. 2017;35(Suppl. 15) https://doi.org/10.1200/JCO.2017.35.15_suppl.506.

  30. Merck. Merck’s KEYTRUDA® (pembrolizumab) in combination with chemotherapy met primary endpoint of pathological complete response (pCR) in pivotal phase 3 KEYNOTE-522 trial in patients with triple-negative breast cancer (TNBC). merck.com. https://investors.merck.com/news/press-release-details/2019/Mercks-KEYTRUDA-pembrolizumab-in-Combination-with-Chemotherapy-Met-Primary-Endpoint-of-Pathological-Complete-Response-pCR-in-Pivotal-Phase-3-KEYNOTE-522-Trial-in-Patients-with-Triple-Negative-Breast-Cancer-TNBC/default.aspx. Published July 29, 2019. Accessed 1 Aug 2019.

  31. Park JW, Liu MC, Yee D, et al. Adaptive randomization of neratinib in early breast cancer. N Engl J Med. 2016;375(1):11–22. https://doi.org/10.1056/NEJMoa1513750.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Rugo HS, Olopade OI, DeMichele A, et al. Adaptive randomization of veliparib–carboplatin treatment in breast cancer. N Engl J Med. 2016;375(1):23–34. https://doi.org/10.1056/NEJMoa1513749.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Esserman L, Hylton N, Asare S, et al. I-SPY2: unlocking the potential of the platform trial. In: Antonijevic Z, Beckman RA, editors. Platform trial designs in drug development umbrella trials and basket trials; 2018. p. 3–22.

    Chapter  Google Scholar 

  34. Prowell TM, Pazdur R. Pathological complete response and accelerated drug approval in early breast cancer. N Engl J Med. 2012;366(26):2438–41. https://doi.org/10.1056/NEJMp1205737.

    Article  CAS  PubMed  Google Scholar 

  35. Masuda N, Lee S-J, Ohtani S, et al. Adjuvant capecitabine for breast cancer after preoperative chemotherapy. N Engl J Med. 2017;376(22):2147–59. https://doi.org/10.1056/NEJMoa1612645.

    Article  CAS  PubMed  Google Scholar 

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Stock, P.G., Mukhtar, R., Ghersin, H., Stover Fiscalini, A., Esserman, L. (2020). Pragmatic Trials and Approaches to Transforming Care. In: Pawlik, T., Sosa, J. (eds) Clinical Trials. Success in Academic Surgery. Springer, Cham. https://doi.org/10.1007/978-3-030-35488-6_6

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  • DOI: https://doi.org/10.1007/978-3-030-35488-6_6

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