Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Decision-analytic modeling as a tool for selecting optimal therapy incorporating hematopoietic stem cell transplantation in patients with hematological malignancy

Abstract

Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is the only curative treatment available for various hematological malignancies. Ideally, prospective randomized controlled trials (RCTs) should establish the indications for allo-HSCT. In reality, however, RCTs are not feasible due to the rarity of many hematological conditions. In these scenarios, decision analysis, which simulates possible outcomes with different approaches, can help in selecting the treatment strategy predicted to have the best result. Assessment of cost-effectiveness can also be incorporated in computational simulation analysis. In this review, we would like to provide an overview of decision-analytic models that evaluate alternative treatment strategies for patients with hematological malignancies.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Markov-state diagram.
Fig. 2: A simulation analysis assessing the impact of change in the timing of up-front allo-HSCT in patients with aggressive ATL.

Similar content being viewed by others

References

  1. Thiruvenkatachari B. Randomized controlled trials: the technique and challenges. J Indian Orthodontic Soc. 2019;49:42–47.

    Google Scholar 

  2. Gale RP, Zhang MJ. Statistical analyses of clinical trials in haematopoietic cell transplantation or why there is a strong correlation between people drowning after falling out of a fishing boat and marriage rate in Kentucky. Bone Marrow Transplant. 2020;55:1–3.

  3. Kim H, Goodall S, Liew D. Health technology assessment challenges in oncology: 20 years of value in health. Value Health. 2019;22:593–600.

    PubMed  Google Scholar 

  4. Bullement A, Cranmer HL, Shields GE. A review of recent decision-analytic models used to evaluate the economic value of cancer treatments. Appl Health Econ Health Policy. 2019;17:771–80.

    PubMed  PubMed Central  Google Scholar 

  5. Kadom N, Itri JN, Trofimova A, Otero HJ, Horny M. Cost-effectiveness analysis: an overview of key concepts, recommendations, controversies, and pitfalls. Acad Radio. 2019;26:534–41.

    Google Scholar 

  6. Hogendoorn W, Moll FL, Sumpio BE, Hunink MG. Clinical decision analysis and markov modeling for surgeons: an introductory overview. Ann Surg. 2016;264:268–74.

    PubMed  Google Scholar 

  7. Barr RD, Sala A. Quality-adjusted survival: a rigorous assessment of cure after cancer during childhood and adolescence. Pediatr Blood Cancer. 2005;44:201–4.

    PubMed  Google Scholar 

  8. Sassi F. Calculating QALYs, comparing QALY and DALY calculations. Health Policy Plan. 2006;21:402–8.

    PubMed  Google Scholar 

  9. Standfield L, Comans T, Scuffham P. Markov modeling and discrete event simulation in health care: a systematic comparison. Int J Technol Assess Health Care. 2014;30:165–72.

    PubMed  Google Scholar 

  10. Tsoi B, Goeree R, Jegathisawaran J, Tarride JE, Blackhouse G, O’Reilly D. Do different decision-analytic modeling approaches produce different results? A systematic review of cross-validation studies. Expert Rev Pharmacoecon Outcomes Res. 2015;15:451–63.

    PubMed  Google Scholar 

  11. Tsoi B, O’Reilly D, Jegathisawaran J, Tarride JE, Blackhouse G, Goeree R. Systematic narrative review of decision frameworks to select the appropriate modelling approaches for health economic evaluations. BMC Res Notes. 2015;8:244.

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Ramos MC, Barton P, Jowett S, Sutton AJ. A systematic review of research guidelines in decision-analytic modeling. Value Health. 2015;18:512–29.

    PubMed  Google Scholar 

  13. Karnon J, Brown J. Selecting a decision model for economic evaluation: a case study and review. Health Care Manag Sci. 1998;1:133–40.

    CAS  PubMed  Google Scholar 

  14. Caro JJ, Moller J. Advantages and disadvantages of discrete-event simulation for health economic analyses. Expert Rev Pharmacoecon Outcomes Res. 2016;16:327–9.

    PubMed  Google Scholar 

  15. Williams C, Lewsey JD, Mackay DF, Briggs AH. Estimation of survival probabilities for use in cost-effectiveness analyses: a comparison of a multi-state modeling survival analysis approach with partitioned survival and Markov decision-analytic modeling. Med Decis Mak. 2017;37:427–39.

    Google Scholar 

  16. Sonnenberg FA, Beck JR. Markov models in medical decision making: a practical guide. Med Decis Mak. 1993;13:322–38.

    CAS  Google Scholar 

  17. Groenwold RH, Sterne JA, Lawlor DA, Moons KG, Hoes AW, Tilling K. Sensitivity analysis for the effects of multiple unmeasured confounders. Ann Epidemiol. 2016;26:605–11.

    PubMed  Google Scholar 

  18. Hollman C, Paulden M, Pechlivanoglou P, McCabe C. A comparison of four software programs for implementing decision analytic cost-effectiveness models. Pharmacoeconomics. 2017;35:817–30.

    PubMed  Google Scholar 

  19. McManus E, Sach TH, Levell NJ. An introduction to the methods of decision-analytic modelling used in economic evaluations for dermatologists. J Eur Acad Dermatol Venereol. 2019;33:1829–36.

    CAS  PubMed  Google Scholar 

  20. Tran G, Zafar SY. Financial toxicity and implications for cancer care in the era of molecular and immune therapies. Ann Transl Med. 2018;6:166.

    PubMed  PubMed Central  Google Scholar 

  21. Centers for Medicare & Medicaid Services. National Health Expenditure Data. https://www.cms.gov/Research-Statistics-Data-and-Systems/Statistics-Trends-and-Reports/NationalHealthExpendData/index.

  22. Drummond MF, Mason AR. European perspective on the costs and cost-effectiveness of cancer therapies. J Clin Oncol. 2007;25:191–5.

    PubMed  Google Scholar 

  23. Elsada A, Doss S, Robertson J, Adam EJ. NICE guidance on trastuzumab emtansine for HER2-positive advanced breast cancer. Lancet Oncol. 2016;17:143–4.

    PubMed  Google Scholar 

  24. Mikudina B, Goodall M, Adler AI. NICE guidance on ibrutinib for previously treated chronic lymphocytic leukaemia and untreated chronic lymphocytic leukaemia in the presence of 17p deletion or TP53 mutation. Lancet Oncol. 2017;18:289–90.

    PubMed  Google Scholar 

  25. Versteegh M, Knies S, Brouwer W. From good to better: new dutch guidelines for economic evaluations in healthcare. Pharmacoeconomics. 2016;34:1071–4.

    PubMed  Google Scholar 

  26. Dyer M, Richardson J, Robertson J, Adam J. NICE guidance on bevacizumab in combination with paclitaxel and carboplatin for the first-line treatment of advanced ovarian cancer. Lancet Oncol. 2013;14:689–90.

    CAS  PubMed  Google Scholar 

  27. Prasad V, De Jesus K, Mailankody S. The high price of anticancer drugs: origins, implications, barriers, solutions. Nat Rev Clin Oncol. 2017;14:381–90.

    PubMed  Google Scholar 

  28. Weinstein MC. How much are Americans willing to pay for a quality-adjusted life year? Med Care. 2008;46:343–5.

    PubMed  Google Scholar 

  29. Neumann PJ, Cohen JT, Weinstein MC. Updating cost-effectiveness-the curious resilience of the $50,000-per-QALY threshold. N Engl J Med. 2014;371:796–7.

    CAS  PubMed  Google Scholar 

  30. Lin JK, Muffly LS, Spinner MA, Barnes JI, Owens DK, Goldhaber-Fiebert JD. Cost effectiveness of chimeric antigen receptor T-cell therapy in multiply relapsed or refractory adult large B-cell lymphoma. J Clin Oncol. 2019;37:2105–19.

    CAS  PubMed  Google Scholar 

  31. Lin JK, Lerman BJ, Barnes JI, Boursiquot BC, Tan YJ, Robinson AQL, et al. Cost effectiveness of chimeric antigen receptor T-Cell therapy in relapsed or refractory pediatric B-Cell acute lymphoblastic leukemia. J Clin Oncol. 2019;37:2105–19.

  32. Huntington SF, von Keudell G, Davidoff AJ, Gross CP, Prasad SA. Cost-effectiveness analysis of brentuximab vedotin with chemotherapy in newly diagnosed stage III and IV hodgkin lymphoma. J Clin Oncol. 2018;36:JCO1800122. [Online ahead of print].

  33. Cameron D, Ubels J, Norstrom F. On what basis are medical cost-effectiveness thresholds set? Clashing opinions and an absence of data: a systematic review. Glob Health Action. 2018;11:1447828.

    PubMed  PubMed Central  Google Scholar 

  34. Neumann PJ, Cohen JT. QALYs in 2018-Advantages and Concerns. JAMA. 2018;319:2473–4.

    PubMed  Google Scholar 

  35. Flowers CR, Ramsey SD. What can cost-effectiveness analysis tell us about chimeric antigen receptor T-cell therapy for relapsed acute lymphoblastic leukemia? J Clin Oncol. 2018:JCO2018793570. [Online ahead of print].

  36. Cook LB, Fuji S, Hermine O, Bazarbachi A, Ramos JC, Ratner L, et al. Revised adult T-cell leukemia-lymphoma international consensus meeting report. J Clin Oncol. 2019;37:677–87.

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Fuji S, Yamaguchi T, Inoue Y, Utsunomiya A, Moriuchi Y, Uchimaru K, et al. Development of a modified prognostic index for patients with aggressive adult T-cell leukemia-lymphoma aged 70 years or younger: possible risk-adapted management strategies including allogeneic transplantation. Haematologica. 2017;102:1258–65.

    CAS  PubMed  PubMed Central  Google Scholar 

  38. Fuji S, Kurosawa S, Inamoto Y, Murata T, Utsunomiya A, Uchimaru K, et al. Role of up-front allogeneic hematopoietic stem cell transplantation for patients with aggressive adult T-cell leukemia-lymphoma: a decision analysis. Bone Marrow Transpl. 2018;53:905–8.

    CAS  Google Scholar 

  39. Fuji S, Inoue Y, Utsunomiya A, Moriuchi Y, Uchimaru K, Choi I, et al. Pretransplantation anti-CCR4 antibody mogamulizumab against adult T-cell leukemia/lymphoma is associated with significantly increased risks of severe and corticosteroid-refractory graft-versus-host disease, nonrelapse mortality, and overall mortality. J Clin Oncol. 2016;34:3426–33.

    PubMed  Google Scholar 

  40. Fuji S, Kurosawa S, Inamoto Y, Murata T, Utsunomiya A, Uchimaru K, et al. A decision analysis comparing unrelated bone marrow transplantation and cord blood transplantation in patients with aggressive adult T-cell leukemia-lymphoma. Int J Hematol. 2019. https://doi.org/10.1007/s12185-019-02777-w. [Epub ahead of print].

  41. Cutler CS, Lee SJ, Greenberg P, Deeg HJ, Perez WS, Anasetti C, et al. A decision analysis of allogeneic bone marrow transplantation for the myelodysplastic syndromes: delayed transplantation for low-risk myelodysplasia is associated with improved outcome. Blood. 2004;104:579–85.

    CAS  PubMed  Google Scholar 

  42. Koreth J, Pidala J, Perez WS, Deeg HJ, Garcia-Manero G, Malcovati L, et al. Role of reduced-intensity conditioning allogeneic hematopoietic stem-cell transplantation in older patients with de novo myelodysplastic syndromes: an international collaborative decision analysis. J Clin Oncol. 2013;31:2662–70.

    PubMed  PubMed Central  Google Scholar 

  43. Della Porta MG, et al. Decision analysis of allogeneic hematopoietic stem cell transplantation for patients with myelodysplastic syndrome stratified according to the revised International Prognostic Scoring System. Leukemia. 2017;31:2449–57. https://doi.org/10.1038/leu.2017.88. Epub 21 Mar 2017.

  44. Greenberg PL, Tuechler H, Schanz J, Sanz G, Garcia-Manero G, Sole F, et al. Revised international prognostic scoring system for myelodysplastic syndromes. Blood. 2012;120:2454–65.

    CAS  PubMed  PubMed Central  Google Scholar 

  45. Koreth J, Schlenk R, Kopecky KJ, Honda S, Sierra J, Djulbegovic BJ, et al. Allogeneic stem cell transplantation for acute myeloid leukemia in first complete remission: systematic review and meta-analysis of prospective clinical trials. JAMA. 2009;301:2349–61.

    CAS  PubMed  PubMed Central  Google Scholar 

  46. Kurosawa S, Yamaguchi T, Miyawaki S, Uchida N, Kanamori H, Usuki K, et al. A Markov decision analysis of allogeneic hematopoietic cell transplantation versus chemotherapy in patients with acute myeloid leukemia in first remission. Blood. 2011;117:2113–20.

    CAS  PubMed  Google Scholar 

  47. Labopin M, Ruggeri A, Gorin NC, Gluckman E, Blaise D, Mannone L, et al. Cost-effectiveness and clinical outcomes of double versus single cord blood transplantation in adults with acute leukemia in France. Haematologica. 2014;99:535–40.

    PubMed  PubMed Central  Google Scholar 

  48. Debals-Gonthier M, Siani C, Faucher C, Touzani R, Lemarie-Basset C, Chabannon C, et al. Cost-effectiveness analysis of haploidentical vs matched unrelated allogeneic hematopoietic stem cells transplantation in patients older than 55 years. Bone Marrow Transplant. 2018;53:1096–104.

    CAS  PubMed  Google Scholar 

  49. Jazic I, Schrag D, Sargent DJ, Haneuse S. Beyond composite endpoints analysis: semicompeting risks as an underutilized framework for cancer research. J Natl Cancer Inst. 2016;108:djw154.

    PubMed  PubMed Central  Google Scholar 

  50. Ford I, Norrie J. Pragmatic trials. N Engl J Med. 2016;375:454–63.

    PubMed  Google Scholar 

  51. Rouse B, Chaimani A, Li T. Network meta-analysis: an introduction for clinicians. Intern Emerg Med. 2017;12:103–11.

    PubMed  Google Scholar 

  52. Shouval R, Bonifazi F, Fein J, Boschini C, Oldani E, Labopin M, et al. Validation of the acute leukemia-EBMT score for prediction of mortality following allogeneic stem cell transplantation in a multi-center GITMO cohort. Am J Hematol. 2017;92:429–34.

    PubMed  Google Scholar 

  53. Shouval R, Labopin M, Bondi O, Mishan-Shamay H, Shimoni A, Ciceri F, et al. Prediction of allogeneic hematopoietic stem-cell transplantation mortality 100 days after transplantation using a machine learning algorithm: a european group for blood and marrow transplantation acute leukemia working party retrospective data mining study. J Clin Oncol. 2015;33:3144–51.

    PubMed  Google Scholar 

  54. Shouval R, Ruggeri A, Labopin M, Mohty M, Sanz G, Michel G, et al. An integrative scoring system for survival prediction following umbilical cord blood transplantation in acute leukemia. Clin Cancer Res. 2017;23:6478–86.

    PubMed  Google Scholar 

Download references

Acknowledgements

This study was supported by Health, Labour, and Welfare Science Grants for Research on Measures for Rare and Intractable Diseases from the Japanese Government.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shigeo Fuji.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fuji, S., Nagler, A., Mohty, M. et al. Decision-analytic modeling as a tool for selecting optimal therapy incorporating hematopoietic stem cell transplantation in patients with hematological malignancy. Bone Marrow Transplant 55, 1220–1228 (2020). https://doi.org/10.1038/s41409-020-0784-x

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41409-020-0784-x

Search

Quick links