Advertisement

Decision-Making in Sequential Adaptive Clinical Trials, with Implications for Drug Misclassification and Resource Allocation

  • Alba C. Rojas-CordovaEmail author
  • Ebru K. Bish
  • Niyousha Hosseinichimeh
Chapter
Part of the Women in Engineering and Science book series (WES)

Abstract

Sequential adaptive clinical trials for new drugs and treatment options represent flexible designs that allow for an earlier-than-planned trial termination, due to established benefit or futility. This novel and innovative approach to drug testing has the potential to accelerate patient access to new therapies and reduce expenditures on drug development. However, this approach also complicates the resource allocation (patient enrollment) and trial termination decisions, which, in turn, impact the probability of misclassification and time-to-market for the new drug, as well as the firm’s profit. In this chapter, we review current practices and the state of the art of sequential designs for adaptive clinical trials with binary response, present novel mathematical models that we have developed to address the resource allocation and trial termination decisions in these trials, and discuss their implications on public policy. We conclude with a discussion of the challenges and the opportunities for future research in this area.

Notes

Acknowledgements

We would like to thank Mr. Keith Gardner, Senior Director of Decision Science at AstraZeneca Pharmaceuticals, for many valuable discussions that improved our understanding of the drug R&D process. This research was supported in part by the Seth Bonder Foundation.

References

  1. Ahuja V, Birge JR (2016) Response-adaptive designs for clinical trials: simultaneous learning from multiple patients. Eur J Oper Res 248(2):619–633zbMATHGoogle Scholar
  2. American Society of Clinical Oncology (ASCO) (2015) Phases of Clinical Trials. http://www.cancer.net/navigating-cancer-care/how-cancer-treated/clinical-trials/phases-clinical-trials. Accessed 28 Sept 2016
  3. Barker A, Sigman C, Kelloff G, Hylton N, Berry D, Esserman L (2009) I-SPY 2: An Adaptive Breast Cancer Trial Design in the Setting of Neoadjuvant Chemotherapy. Clin Pharmacol Ther 86(1):97–100Google Scholar
  4. Bassler D, Montori VM, Briel M, Glasziou P, Guyatt G (2008) Early stopping of randomized clinical trials for overt efficacy is problematic. J Clin Epidemiol 61(3):241–246Google Scholar
  5. Bellissant E, Benichou J, Chastang C (1990) Application of the triangular test to phase II cancer clinical trials. Stat. Med 9(8):907–917Google Scholar
  6. Berndt ER, Nass D, Kleinrock M, Aitken M (2015) Decline in economic returns from new drugs raises questions about sustaining innovations. Health Aff 34(2):245–252Google Scholar
  7. Berry DA (2005) Introduction to Bayesian methods III: use and interpretation of Bayesian tools in design and analysis. Clin. Trials 2(4):295–300Google Scholar
  8. Berry DA (2011) Adaptive clinical trials: the promise and the caution. J Clin Oncol 29(6):606–609Google Scholar
  9. Berry DA (2012) Adaptive clinical trials in oncology. Nat Rev Clin Oncol 9(4):199–207Google Scholar
  10. Berry DA, Fristedt B (1985) Bandit problems: sequential allocation of experiments (Monographs on Statistics and Applied Probability). Springer, DordrechtzbMATHGoogle Scholar
  11. Berry SM, Carlin BP, Lee JJ, Muller P (2010) Bayesian adaptive methods for clinical trials. CRC Press, Boca RatonzbMATHGoogle Scholar
  12. Chow SC, Chang M (2008) Adaptive design methods in clinical trials–a review. Orphanet J Rare Dis 3(1):1Google Scholar
  13. Christian B, Cremaschi S (2015) Heuristic solution approaches to the pharmaceutical R&D pipeline management problem. Comput Chem Eng 74:34–47Google Scholar
  14. Colvin M, Maravelias CT (2010) Modeling methods and a branch and cut algorithm for pharmaceutical clinical trial planning using stochastic programming. Eur J Oper Res 203(1):205–215zbMATHGoogle Scholar
  15. Crowley J, Hoering A (2012) Handbook of statistics in clinical oncology. CRC Press, Boca RatonzbMATHGoogle Scholar
  16. David E, Tramontin T, Zemmel R (2009) Pharmaceutical R&D: the road to positive returns. Nat Rev Drug Discov 8(8):609–610Google Scholar
  17. David FS, Bobulsky S, Schulz K, Patel N (2015) Creating value with financially adaptive clinical trials. Nat Rev Drug Discov 14(8):523–524Google Scholar
  18. Denrell J, March JG (2001) Adaptation as information restriction: The hot stove effect. Organ Sci 12(5):523–538Google Scholar
  19. DiMasi JA, Hansen RW, Grabowski HG (2003) The price of innovation: new estimates of drug development costs. J. Health Econ 22(2):151–185, URL http://www.sciencedirect.com/science/article/pii/S0167629602001261 Google Scholar
  20. DiMasi JA, Grabowski HG, Hansen RW (2016) Innovation in the pharmaceutical industry: new estimates of R&D costs. J Health Econ 47:20–33Google Scholar
  21. Ding M, Eliashberg J, Stremersch S (2013) Innovation and marketing in the pharmaceutical industry: emerging practices, research, and policies. Springer, New YorkGoogle Scholar
  22. Enea G, Lo Nigro G (2011) A real options based model to select a balanced R&D portfolio. In: 15th Annual International Conference on Real Options, Turku, Finland, Realoptions.orgGoogle Scholar
  23. Gordon Lan K, Simon R, Halperin M (1982) Stochastically curtailed tests in long term clinical trials. Seq Anal 1(3):207–219MathSciNetzbMATHGoogle Scholar
  24. Halliday RG, Drasdo AL, Lumley CE, Walker SR (1997) The allocation of resources for R&D in the world’s leading pharmaceutical companies. R D Manag 27(1):63–77Google Scholar
  25. Hulot JS, Cucherat M, Charlesworth A, Van Veldhuisen DJ, Corvol JC, Mallet A, Boissel JP, Hampton J, Lechat P (2003) Planning and monitoring of placebo-controlled survival trials: comparison of the triangular test with usual interim analyses methods. Br J Clin Pharmacol 55(3):299–306Google Scholar
  26. Jacob WF, Kwak YH (2003) In search of innovative techniques to evaluate pharmaceutical R&D projects. Technovation 23(4):291–296Google Scholar
  27. Jitlal M, Khan I, Lee S, Hackshaw A (2012) Stopping clinical trials early for futility: retrospective analysis of several randomised clinical studies. Br J Cancer 107(6):910–917Google Scholar
  28. Kaminsky P, Yuen M (2014) Production capacity investment with data updates. IIE Trans 46(7):664–682Google Scholar
  29. Kouvelis P, Milner J, Tian Z (2017) Clinical trials for new drug development: Optimal investment and application. Manuf Serv Oper Manag 19(3):437–452Google Scholar
  30. Lacey MJ, Hanna GJ, Miller JD, Foster TS, Russell MW (2014) Impact of pharmaceutical innovation in HIV/AIDS treatment during the highly active antiretroviral therapy (HAART) era in the US, 1987–2010: an epidemiologic and cost-impact modeling case study. http://truvenhealth.com/Portals/0/Assets/Life-Sciences/White-Papers/pharma-innovation-hiv-aids-treatment.pdf. Accessed 20 Jan 2018
  31. Levis AA, Papageorgiou LG (2004) A hierarchical solution approach for multi-site capacity planning under uncertainty in the pharmaceutical industry. Comput Chem Eng 28(5):707–725Google Scholar
  32. Macready WG, Wolpert DH (1998) Bandit problems and the exploration/exploitation tradeoff. IEEE Trans Evol Comput 2(1):2–22Google Scholar
  33. Madani O, Lizotte DJ, Greiner R (2004) The budgeted multi-armed bandit problem. In: International conference on computational learning theory, Springer, Berlin pp 643–645Google Scholar
  34. McPherson K (1982) On choosing the number of interim analyses in clinical trials. Stat Med 1(1):25–36Google Scholar
  35. Mueller PS, Montori VM, Bassler D, Koenig BA, Guyatt GH (2007) Ethical issues in stopping randomized trials early because of apparent benefit. Ann Intern Med 146(12):878–881Google Scholar
  36. National Cancer Institute (2014) Surveillance, epidemiology, and end results program. https://seer.cancer.gov/statfacts/html/ld/all.html. Accessed 02 Apr 2017
  37. National Cancer Institute (NCI) (2015) NCI dictionary of cancer terms. https://www.cancer.gov/publications/dictionaries/cancer-terms?cdrid=45833. Accessed 28 Sept 2016
  38. National Center for Health Statistics (2015) Health, United States, 2014: with special feature on adults aged 55–64. https://www.cdc.gov/nchs/data/hus/hus14.pdf. Accessed 21 May 2017
  39. Nesse E (2016) Clinical trial design. https://ccrod.cancer.gov/confluence/download/attachments/71041052/Clinical_Trial_Design.pdf. Accessed 28 Sept 2016
  40. Nissen M (2016) Read the label on pharma’s new drug sales. https://www.bloomberg.com/gadfly/articles/2016-08-16/big-pharma-new-drug-sales-tell-only-part-of-the-story. Accessed 29 Sept 2016
  41. O’Brien PC, Fleming TR (1979) A multiple testing procedure for clinical trials. Biometrics 35(3):549–556Google Scholar
  42. Oh HC, Karimi I (2004) Regulatory factors and capacity-expansion planning in global chemical supply chains. Ind Eng Chem Res 43(13):3364–3380Google Scholar
  43. Orloff J, Douglas F, Pinheiro J, Levinson S, Branson M, Chaturvedi P, Ette E, Gallo P, Hirsch G, Mehta C, et al (2009) The future of drug development: advancing clinical trial design. Nat Rev Drug Discov 8(12):949–957Google Scholar
  44. Patel NR, Ankolekar S, Antonijevic Z, Rajicic N (2013) A mathematical model for maximizing the value of phase 3 drug development portfolios incorporating budget constraints and risk. Stat Med 32(10):1763–1777MathSciNetGoogle Scholar
  45. Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC, Munos BH, Lindborg SR, Schacht AL (2010) How to improve R&D productivity: the pharmaceutical industry’s grand challenge. Nat Rev Drug Discov 9(3):203–214Google Scholar
  46. Pharmaceutical Research and Manufacturers of America (PhRMA) (2015) Biopharmaceutical industry-sponsored clinical trials: impact on state economies. http://www.phrma.org/sites/default/files/pdf/biopharmaceutical-industry-sponsored-clinical-trials-impact-on-state-economies.pdf. Accessed 28 Sept 2016
  47. Pharmaceutical Research and Manufacturers of America (2016a) 2016 Biopharmaceutical research industry profile. http://www.phrma.org/report/industry-profile-2016. Accessed 21 May 2017
  48. Pharmaceutical Research and Manufacturers of America (2016b) A decade of innovation in chronic diseases. http://www.phrma.org/report/a-decade-of-innovation-in-chronic-diseases. Accessed 21 May 2017
  49. Pocock SJ (1977) Group sequential methods in the design and analysis of clinical trials. Biometrika 64(2):191–199Google Scholar
  50. Pocock SJ (1982) Interim analyses for randomized clinical trials: the group sequential approach. Biometrics 38(1):153–162MathSciNetGoogle Scholar
  51. Pocock S, White I (1999) Trials stopped early: too good to be true? The Lancet 353(9157):943–944Google Scholar
  52. President’s Council of Advisors on Science and Technology (2012) Report to the President on propelling innovation in drug discovery, development, and evaluation. https://www.whitehouse.gov/sites/default/files/microsites/ostp/pcast-fda-final.pdf. Accessed 12 Oct 2016
  53. Rajapakse A, Titchener-Hooker NJ, Farid SS (2005) Modeling of the biopharmaceutical drug development pathway and portfolio management. Comput Chem Eng 29(6):1357–1368Google Scholar
  54. Rogers MS, Chang AM, Todd S (2005) Using group-sequential analysis to achieve the optimal sample size. BJOG Int J Obstet Gynaecol 112(5):529–533Google Scholar
  55. Rojas-Cordova A, Bish EK (2018) Optimal patient enrollment in sequential adaptive clinical trials with binary response, Working paperGoogle Scholar
  56. Rojas-Cordova A, Hosseinichimeh N (2018) Trial termination and drug misclassification in sequential adaptive clinical trials. Service Science 10(3):354–377Google Scholar
  57. Sebille V, Bellissant E (2000) Comparison of four sequential methods allowing for early stopping of comparative clinical trials. Clin Sci 98(5):569–578Google Scholar
  58. Solak S, Clarke JPB, Johnson EL, Barnes ER (2010) Optimization of R&D project portfolios under endogenous uncertainty. Eur J Oper Res 207(1):420–433MathSciNetzbMATHGoogle Scholar
  59. Sun E, Lakdawalla D, Reyes C, Goldman D, Philipson T, Jena A (2008) The determinants of recent gains in cancer survival: an analysis of the Surveillance, Epidemiology, and End Results (SEER) database. J Clin Oncol 26(15):6616–6616Google Scholar
  60. Todd S, Whitehead A, Stallard N, Whitehead J (2001) Interim analyses and sequential designs in phase III studies. Br J Clin Pharmacol 51(5):394–399Google Scholar
  61. US Congress (2016) H.R.34 - 21st century cures act. https://www.congress.gov/bill/114th-congress/house-bill/34/. Accessed 21 Jan 2018
  62. US Food and Drug Administration (2010) Guidance for industry: adaptive design clinical trials for drugs and biologics. Food and Drug Administration, Washington DCGoogle Scholar
  63. Whitehead J (1997) The design and analysis of sequential clinical trials. Wiley, ChichesterzbMATHGoogle Scholar
  64. Whitehead J (2002) Sequential methods in clinical trials. Seq Anal 21(4):285–308MathSciNetzbMATHGoogle Scholar
  65. Whitehead J (2004) Stopping clinical trials by design. Nat Rev Drug Discov 3(11):973–977Google Scholar
  66. Ye F (2008) Design and analysis of phase III clinical trials. https://medschool.vanderbilt.edu/cqs/files/cqs/media/2008Jun2008Fei.pdf. Accessed 28 Sept 2016
  67. Zannad F, Stough WG, McMurray JJ, Remme WJ, Pitt B, Borer JS, Geller NL, Pocock SJ (2012) When to stop a clinical trial early for benefit: lessons learned and future approaches. Circ Heart Fail 5(2):294–302Google Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Alba C. Rojas-Cordova
    • 1
    Email author
  • Ebru K. Bish
    • 2
  • Niyousha Hosseinichimeh
    • 2
  1. 1.Department of EMISSouthern Methodist UniversityDallasUSA
  2. 2.Grado Department of Industrial and Systems EngineeringVirginia TechBlacksburgUSA

Personalised recommendations