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Decision modelling for the evaluation of diabetes outcomes

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The process of decision modelling in diabetes mellitus (DM) is often complicated by comorbidity among diabetic patients, complexity of endpoint selection, and unclear time horizons. Aim. To review the available recommendations, relevant methods and mathematical approaches to decision modelling in DM. Materials and Methods. We searched through the PubMed database using the ResearchGate and Mendeley networks; we also collected data from the websites of the key opinion leaders in the field of pharmacoeconomics and decision modelling. Results. This review contains up-to-date information on the validity of the most common DM decision models and on the validity of extrapolating the type 2 DM models to patients with type 1 DM. We also provide some clinically relevant comments on the American Diabetes Association’s requirements concerning the decision models in DM. The review incorporates data on the current mathematical approaches to modelling the changes in glycated hemoglobin levels, the body mass index and the quality-adjusted life expectancy – for both type 1 and type 2 DM. Conclusion. Despite recent successes in DM decision modelling, the existing approaches are not always relevant to some groups of DM patients or to some aspects of the disease. Thus, the use of the novel anti-diabetic drugs (liraglutide, semaglutide, empagliflozin) capable of significantly reducing cardiovascular risks in DM patients, require new approaches to decision modelling in diabetes mellitus. 

About the Authors

A. A. Mosikian
Federal Almazov North West Medical Research Centre
Russian Federation

Mosikian Anna Albertovna – Medical Resident at the Dpt. of Internal diseases and Endocrinology.

Address: ul. Akkuratova, 2, St. Petersburg, Russia, 197341.

W. Zhao
Pavlov First Saint Petersburg State Medical University
Russian Federation

Zhao Wenlong – PhD student at the Dpt. of Clinical pharmacology and evidence-based medicine

Address: ul. L’va Tolstogo, 6-8, St. Petersburg, Russia, 197022.

T. L. Galankin
Федеральное государственное бюджетное образовательное учреждение высшего образования «Первый Санкт-Петербургский государственный медицинский университет имени акад. И. П. Павлова» Минздрава России
Russian Federation

Pavlov First Saint Petersburg State Medical University

A. S. Kolbin
Pavlov First Saint Petersburg State Medical University: Saint Petersburg State University

Kolbin Aleksei Sergeevich – MD, Professor, Head of the Dpt. of Clinical pharmacology and evidence-based medicine, First St. Petersburg State Medical University; Professor at the Dpt. of Pharmacology, St. Petersburg State University.

Address: 21-Line, 8, VO, St. Petersburg, Russia, 199106. 


1. List of medicines registered on the territory of the Russian Federation [Perechen’ zaregistrirovannykh na territorii RF lekarstvennykh preparatov (in Russian)]. URL: http://grls.rosminzdrav. ru/grls.aspx. Accessed: 05.07.2017.

2. Fitchett D., Cheng A., Connelly K. et al. A Practical Guide to the Use of Glucose-Lowering Agents With Cardiovascular Benefit or Proven Safety. Can J Cardiol. 2017; 33 (7): 940-942.

3. Standl E., Schnell O., McGuire D.K., et al. Integration of recent evidence into management of patients with atherosclerotic cardiovascular disease and type 2 diabetes. Lancet Diabetes Endocrinol. 2017; 5 (5): 391-402.

4. Mosikyan A. A., Chzhao V., Galankin T. L., Kolbin A. S. Issledovaniya EMPA-REG OUTCOME, LEADER i SUSTAIN-6: vozmozhnye mekhanizmy snizheniya serdechno-sosudistykh riskov novymi sakharosnizhayushchimi preparatami. Klinicheskaya farmakologiya i terapiya (in Russian). 2017; 26 (2): 77-82.

5. Kolbin A. S., Kurylev A. A., Proskurin M. A., Balykina Yu. E. Modelirovanie meditsinskikh i ekonomicheskikh iskhodov sakharnogo diabeta. Analiz primenyaemykh v mire matematicheskikh modelei. Klinicheskaya farmakologiya i terapiya (in Russian). 2012; 21 (5): 91-96.

6. International Society for Pharmacoeconomics and Outcomes Research (ISPOR). URL: Accessed: 15.08.2017.

7. American Diabetes Association (ADA). URL: Accessed: 15.08.2017.

8. Briggs A., Claxton C., Sculpher M. Decision modelling for health economic evaluation. Oxford University Press, 2006.

9. Kolbin A. S., Zyryanov S. K., Belousov D. Yu. Basic concepts in the evaluation of medical technologies: a method. allowance. Under the Society. Ed. A. S. Kolbin, S. K. Zyryanov, D. Yu. Belousov. [Osnovnye ponyatiya v otsenke meditsinskikh tekhnologii: metod. posobie. Pod obshch. red. A. S. Kolbina, S. K. Zyryanova, D. Yu. Belousova (in Russian)]. Moscow. 2013; 42 s.

10. Venttsel’ E. S. Probability theory. 6 th ed. sr. [Teoriya veroyatnostei. 6-e izd. ster. (in Russian)]. Moscow. 1999; 576 c.

11. Henriksson M., Jindal R., Sternhufvud C. et al. A Systematic Review of Cost-Effectiveness Models in Type 1 Diabetes Mellitus. Pharmacoeconomics (in Russian). 2016; 34 (6): 569-85.

12. Willis M., Asseburg C., He J. Validation of economic and health outcomes simulation model of type 2 diabetes mellitus (ECHO-T2DM). J Med Econ. 2013; 16 (8): 1007-21.

13. Palmer A. J., Roze S., Valentine W. J. et al. The CORE Diabetes Model: Projecting long-term clinical outcomes, costs and costeffectiveness of interventions in diabetes mellitus (types 1 and 2) to support clinical and reimbursement decision-making. CurrMed Res Opin. 2004; 20 (1): 5-26.

14. Palmer A. J., Roze S., Valentine W. J. et al: Validation of the CORE Diabetes Model against epidemiological and clinical studies. Curr Med Res Opin. 2004; 20 (1): 27-40.

15. Hayes A. J., Leal J., Gray A. M., et al. UKPDS Outcomes Model 2: a new version of a model to simulate lifetime health outcomes of patients with type 2 diabetes mellitus using data from the 30 year United Kingdom Prospective Diabetes Study: UKPDS 82. Diabetologia (in Russian). 2013; 56: 1925-33.

16. Chen J., Alemao E., Yin D., Cook J. Development of a diabetes treatment simulation model: with application to assessing alternative treatment intensification strategies on survival and diabetes-related complications. Diabetes Obes Metab. 2008; 10 (1): 33-42.

17. Mueller E., Maxion-Bergemann S., Gultyaev D. et al. Development and validation of the Economic Assessment of Glycemic Control and Long-Term Effects of diabetes (EAGLE) model. Diabetes Technol Ther. 2006; 8: 219-36.

18. Govan L., Wu O., Lindsay R., Briggs A. How Do Diabetes Models Measure Up? A Review Of Diabetes Economic Models and ADA Guidelines. JHEOR. 2015; 3 (2): 132-52.

19. Kolbin A. S., Khmel’nitskii O.K., Kurylev A. A., Balykina Yu. E., Proskurin M. A., Kolpak E. P., Bure M. V. Pervyi v Rossii opyt postroeniya simulyatsionnoi modeli iskhodov sakharnogo diabeta 2 tipa s diskretirovannym modelirovaniem sobytii. Klinikoekonomicheskaya ekspertiza. FARMAKOEKONOMIKA. Sovremennaya farmakoekonomika i farmakoepidemiologiya / PHARMACOECONOMICS. Modern pharmacoeconomics and pharmacoepidemiology (in Russian). 2013; 6 (2): 24-31.

20. Claudius Ptolemaeus. System Design, Modelling, and Simulation using Ptolemy II. Chapter 9: Continious Time Models. 2014. URL: ContinuousTimeModels.pdf. Accessed: 13.08.2017.

21. Eddy D. M., Schlessinger L. Archimedes: a trial-validated model of diabetes. Diabetes Care. 2003; 26: 3093-101.

22. Model’ UKPDS-OM2. URL: Accessed: 30.06.17.

23. Marso S. P., Daniels G. H., Brown-Frandsen K. et al. Liraglutide and Cardiovascular Outcomes in Type 2 Diabetes. N Engl J Med. 2016; 375 (4): 311-22.

24. Marso S. P., Bain S. C., Consoli A. et al. Semaglutide and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med. 2016; 375 (19): 1834-1844.

25. Zinman B., Wanner C., Lachin J. M. et al. Empagliflozin, cardiovascular outcomes, and mortality in type 2 diabetes. N Engl J Med. 2015; 373: 2117-2128.

26. American Diabetes Association: Guidelines for computer modeling of diabetes and its complications. Diabetes Care. 2004; 27: 2262-5.

27. Eddy D. M., Hollingworth W., Caro J. J. et al. Model transparency and validation: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force-7. Med Decis Making. 2012; 32: 733-43.

28. Methodological recommendations for the evaluation of comparative clinical efficacy and safety of the drug. FGBU “CECMPS” of the Ministry of Health of Russia [Metodicheskie rekomendatsii po otsenke sravnitel’noi klinicheskoi effektivnosti i bezopasnosti lekarstvennogo preparata. FGBU “TsEKKMP” Minzdrava Rossii (in Russian)]. 2016.

29. McEwan P., Bennett H., Qin L., et al. An alternative approach to modelling HbA1c trajectories in patients with type 2 diabetes mellitus. Diabetes Obes Metab. 2017; 19 (5): 628-634.

30. Kind P., Lafata J. E., Matuszewski K. et al. The Use of QALYs in Clinical and Patient Decision-Making: Issues and Prospects. Value Health. 2009; 12 (1): 27-30.

31. Dedov I. I., Shestakova M. V., Galstyan G. R. Rasprostranennost’ sakharnogo diabeta 2 tipa u vzroslogo naseleniya Rossii (issledovanie NATION). Sakharnyi diabet (in Russian). 2016; 19 (2): 104-112.

32. Shestakova M. V., Chazova I. E., Shestakova E. A. Rossiiskoe mnogotsentrovoe skriningovoe issledovanie po vyyavleniyu nediagnostirovannogo sakharnogo diabeta 2 tipa u patsientov s serdechnososudistoi patologiei. Sakharnyi diabet (in Russian). 2016; 19 (1): 24-29.

33. Yamagishi S. I., Nakamura N., Matsui T. Glycation and cardiovascular disease in diabetes: A perspective on the concept of metabolic memory. J Diabetes. 2017; 9 (2): 141-148.

34. McEwan P., Foos V., Lamotte M., Evans M. Quantifying the health economic benefit of key therapeutic outcomes in the management of type 2 diabetes and assessing their inter-relationship. Value Health. 2016; 19 (3): A88.

35. McEwan P., Lamotte M., Grant D., et al. Assessing The Consistency Of Absolute Cardiovascular Risk Prediction And Relative Risk Reduction In Type-2 Diabetes Mellitus. Value Health. 2016; 19 (3): A165.

36. IMS Core Diabetes Model. URL: Accessed: 19.07.2017.

37. Foos V., Grant D., Palmer J. L., et al. The Role of Simulation Modeling in Planning Long-Term Clinical Trials in Type 2 Diabetes. Value Health. 2013; 16 (7): A587-8.

38. Clarke P. M., Gray A. M., Briggs A. et al. A model to estimate the lifetime health outcomes of patients with type 2 diabetes: the United Kingdom Prospective Diabetes Study (UKPDS) outcomes model (UKPDS 68). Diabetologia (in Russian). 2004; 47: 1747-1759.

39. McEwan P., Foos V., Palmer J. L. et al. Therapy Escalation Thresholds and the Potential for Biased Cost Effectiveness Analysis When Failing to Sample Baseline HbA1c in Type 2 Diabetes. Value Health. 2013; 16 (7): A592.

40. National Institute for Health and Care Excellence. Type 2 diabetes in adults: management. NICE guidelines [NG28]. URL: https://www. Accessed: 19.07.2017.

41. Dedov I. I. Algoritmy spetsializirovannoi meditsinskoi pomoshchi bol’nym sakharnym diabetom. Pod redaktsiei I. I. Dedova, M. V. Shestakovoi, A. Yu. Maiorova. 8-i vypusk. Sakharnyi diabet (in Russian). 2017; 20 (1S): 1-121.

42. Mata-Cases M., Franch-Nadal J., Real J. et al. Therapeutic Inertia in Patients Treated With Two or More Antidiabetics in Primary Care: Factors Predicting Intensification of Treatment. Diabetes Obes Metab. 2017 [Epub ahead of print].

43. McEwan P., Foos V., Lamotte M. The Impact of Baseline Hba1c and Hba1c Trajectories on Time to Therapy Escalation In Type 2 Diabetes Mellitus. Value Health. 2015; 18 (7): A698.

44. Elektronnyi kal’kulyator dlya prognozirovaniya izmeneniya urovnya glikirovannogo gemoglobina u patsientov s sakharnym diabetom 2 tipa. URL: dom.12865/full. Data obrashcheniya: 19.07.2017.

45. Bennett H., McEwan P., Bergenheim K., Gordon J. Assessment of unmet clinical need in type 2 diabetic patients on conventional therapy in the UK. Diabetes Ther. 2014; 5 (2): 567-578.

46. Willis M., Asseburg C., Nilsson A. et al. Multivariate Prediction Equations for HbA1c Lowering, Weight Change, and Hypoglycemic Events Associated with Insulin Rescue Medication in Type 2 Diabetes Mellitus: Informing Economic Modeling. Value Health. 2017; 20 (3): 357-371.

47. Glasgow Diabetes Managed Clinical Network. Guidelines for Insulin Initiation and Adjustment in Primary Care in patients with Type 2 Diabetes: for the guidance of Diabetes Specialist Nurses. 2012. 36 p.

48. Petznick A. Insulin management of type 2 diabetes mellitus. Am Fam Physician. 2011; 84 (2): 183-90.

49. AACE/ACE comprehensive diabetes management algorithm. 2015. Endocr Pract. 2015; 21: 438-447.

50. Diabetes Control and Complications Trial. Epidemiology of severe Hypoglycemia in the Diabetes Control and Complications Trial. The DCCT Research Group. Am J Med. 1991; 90: 450-9.

51. DeFronzo R.A., Stonehouse A. H., Han J., Wintle ME. Relationship of baseline HbA1c and efficacy of current glucose-lowering therapies: a meta-analysis of randomized clinical trials. Diabet Med. 2010; 27: 309-17.

52. Riddle M. C., Vlajnic A., Zhou R., Rosenstock J. Baseline HbA1c predicts attainment of 7.0% HbA1c target with structured titration of insulin glargine in type 2 diabetes: a patient-level analysis of 12 studies. Diabetes Obes Metab. 2013; 15: 819-25.

53. Home P. D., Shen C., Hasan M. I. et al. Predictive and explanatory factors of change in HbA1c in a 24-week observational study of 66,726 people with type 2 diabetes starting insulin analogs. Diabetes Care. 2014; 37: 1237-45

54. McEwan P., Foos V., Grant D., et al. Predicting the frequency of severe and non-severe hypoglycaemia in insulin treated type-2 diabetes subjects. Value Health. 2013; 16: A435.

55. Foos V., Lamotte M., McEwan P. The comparison of cardiovascular incidence predictions in Type 1 diabetes utilizing alternative risk prediction models. Value Health. 2016; 19 (3): A86.

56. De Ferranti S. D., de Boer I. H., Fonseca V. et al. Type 1 diabetes mellitus and cardiovascular disease: a scientific statement from the American Heart Association and American Diabetes Association. Diabetes Care. 2014; 37 (10): 2843-63.

57. McEwan P., Foos V., Lamotte M. Contrasting Predictions of Cardiovascular Incidence Derived From Alternative Risk Prediction Models In Type 1 Diabetes. Value Health. 2015; 18 (7): A695.

58. The Mount Hood 4 Modeling Group. Computer Modeling of Diabetes and Its Complications. Diabetes Care. 2007; 30 (6): 16381646.

59. Pambianco G., Costacou T., Ellis D., et al. The 30-year natural history of type 1 diabetes complications: the Pittsburgh Epidemiology of Diabetes Complications Study experience. Diabetes. 2006; 55 (5): 1463-9.

60. Cederholm J., Eeg-Olofsson K., Eliasson B. et al. A new model for 5-year risk of cardiovascular disease in Type 1 diabetes; from the Swedish National Diabetes Register (NDR). Diabet Med. 2011; 28 (10): 1213-20.

61. Alex C. Michalos (Ed.) Encyclopedia of Quality of Life and WellBeing Research Springer, Dordrecht, Netherlands, Springer Reference Series. 2014; 5320-5321.

62. McEwan P., Foos V., Grant D. et al. Drivers Of Cost-Effectiveness In Type-2 Diabetes Mellitus. Value Health. 2013; 16 (3): A165.

63. Bennett H., Bergenheim K., McEwan P. Understanding The Inter-Relationship Between Improved Glycaemic Control, Hypoglycaemia and Weight Change Within A Type 1 Diabetic Population. Value Health. 2015; 18 (7): A610-611.

64. Nordon C., Karcher H., Groenwold R. H. et al. GetReal consortium. The “Efficacy-Effectiveness Gap”: historical background and current conceptualization. Value Health. 2016; 19 (1): 75-81.

65. Ankarfeldt M. Z., Adalsteinsson E., Groenwold R. H.H. et al. A systematic literature review on the efficacy-effectiveness gap: comparison of randomized controlled trials and observational studies of glucose-lowering drugs. Clinical Epidemiology. 2017; 9: 41-51.

66. Bajaj H., Zinman B. Diabetes: Steno-2 – a small study with a big heart. Nat Rev Endocrinol. 2016; 12 (12): 692-694.

67. Perreault L. EMPA-REG OUTCOME: The Endocrinologist’s Point of View. Am J Cardiol. 2017; 120 (1S): 48-52.

68. Pham S. V., Chilton R. J. EMPA-REG OUTCOME: The Cardiologist’s Point of View. Am J Cardiol. 2017; 120 (1S): 53-58.

69. Wanner C. EMPA-REG OUTCOME: The Nephrologist’s Point of View. Am J Cardiol. 2017; 120 (1S): 59-67.

70. Stam F., van Guldener C., Becker A. et al. Endothelial dysfunction contributes to renal function-associated cardiovascular mortality in a population with mild renal insufficiency: the Hoorn study. J Am Soc Nephrol. 2006; 17 (2): 537-45.

71. Ragot S., Saulnier P. J., Velho G. et al.; SURDIAGENE and DIABHYCAR Study Groups. Dynamic changes in renal function are associated with major cardiovascular events in patients with type 2 diabetes. Diabetes Care. 2016; 39: 1259-1266.

72. Herbst R., Bolton W., Shariff A., Green J. B. Cardiovascular Outcome Trial Update in Diabetes: New Evidence, Remaining Questions. Curr Diab Rep. 2017; 17 (9): 67.

73. Bajaj H. S., Zinman B., Verma S. Antihyperglycemic agents and cardiovascular outcomes: recent insights. Curr Opin Cardiol. 2017. [Epub ahead of print]

74. MacEwan J.P., Sheehan J. J., Yin W., et al. The relationship between adherence and total spending among Medicare beneficiaries with type 2 diabetes. Am J Manag Care. 2017; 23 (4): 248-252.

75. Salas M., Hughes D., Zuluaga A. et al. Costs of medication nonadherence in patients with diabetes mellitus: a systematic review and critical analysis of the literature. Value Health. 2009; 12 (6): 91522.


For citations:

Mosikian A.A., Zhao W., Galankin T.L., Kolbin A.S. Decision modelling for the evaluation of diabetes outcomes. FARMAKOEKONOMIKA. Modern Pharmacoeconomics and Pharmacoepidemiology. 2017;10(3):47-58. (In Russ.)

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