Evaluation of the antitumor drugs therapeutic value using the method of multicriterial analysis of decision-making

Aim. Development of a tool for the evaluation of the therapeutic value of anticancer drugs based on multi-criteria analysis of decision making.

Materials and methods. The study included 4 main stages. At the first stage, based on the results of the publications analysis and the EVIDEM system, a list of therapeutic value criteria was formed. At the second stage, for each criterion, a survey of experts was used to develop a scale and to build a value function using the curve-fitting method. At the third stage, the weighing was carried out using the discrete choice experiment method and the statistical package "support.CEs" in R Studio. At the fourth stage, based on the obtained values of the weight coefficients and value functions, a simple linear additive model was constructed.

Results. The authors developed a tool that included 5 criteria for the value of anticancer drugs: efficacy, safety, disease severity, ease of administration, and therapeutic need. For each criterion, a linear value function was built, which allowed the authors to obtain the value of the severity of the criterion in the range from 0 to 1 based on the absolute values of the criterion for a particular drug. As a result of the weighing, two sets of weighting coefficients were obtained by using two different methods of survey results processing. The criterion of efficacy has the most significant contribution to the therapeutic value for the Russian health care system, and the least significant is the severity of the disease. The distribution of the criteria in the intermediate contribution ranks was significantly different between the two sets of weights.

Conclusions. For practical application and implementation of the proposed tool, it is required to conduct the validation using the example of specific antitumor drugs to select a final set of weighting coefficients, check the stability of the results, and form a scale of therapeutic value levels.

1. HTA Glossary. [Electronic resources] URL: http://htaglossary.net/health+technology+assessment. Accessed: 13.07.2020.

2. World Health Organization. Health Technology Assessment. [Electronic resources] URL: https://www.who.int/medical_devices/assessment/en/#:~:text=Health%20technology%20assessment%20(HTA)%20refers,health%20intervention%20or%20health%20technology. Accessed: 25.06.2020.

3. Henshall C., Schuller T. Health technology assessment, value-based decision making, and innovation. Int. J. Technol. Assess. 2013; 29 (4): 353–359.

4. Dolan J.G. Multi-criteria clinical decision support. Patient Patient-Centered Outcomes Res. 2010; 3 (4): 229–248.

5. Federal Law of the Russian Federation of 12.04.2010 No. 61-FZ (red. ot 03.07.2016) "On the Circulation of Medicines". (in Russ).

6. Decree of the Government of the Russian Federation of 28.08.2014 N 871 (ed. Of 20.11.2018) “On approval of the Rules for the formation of the lists of drugs for medical use and the minimum range of drugs needed to provide medical care” [Postanovlenie Pravitel’stva RF ot 28.08.2014 N 871. (in Russ).

7. Marsh K. et al. Multi-criteria decision analysis to support healthcare decisions. Springer. 2017; Vol. 10.

8. Kolasa K., Zah V., Kowalczyk M. How can multi criteria decision analysis support value assessment of pharmaceuticals?-Findings from a systematic literature review. Expert Rev. Pharmacoecon. Outcomes Res. 2018; 18 (4): 379–391.

9. Collaboration E. Decision Criteria Conceptual background, definitions, design & instructions. 2015.

10. Diaby V., Goeree R. How to use multi-criteria decision analysis methods for reimbursement decision-making in healthcare: a step-by-step guide. Expert Rev. Pharmacoecon. Outcomes Res. 2014; 14 (1): 81–99.

11. Aizaki H., Aizaki M.H. Package "support. CEs." 2015.

12. Aizaki H. Basic functions for supporting an implementation of choice experiments in R. J. Stat. Softw. American Statistical Association. 2012; 50: 1–24.