Multiple linear regression analysis of cancer treatment effectiveness at the level of constituent entities in the Russian Federation

Gorkavenko F.V.

Center for Expertise and Quality Control of Medical Care;
Russian Medical Academy of Continuous Professional Education

Scopus AuthorID: 57219030739
ORCID: 0000-0002-4586-2451

Shchurov D.G.

Center for Expertise and Quality Control of Medical Care

Scopus AuthorID: 57222291235
ORCID: 0000-0002-6703-4788

Omelyanovskiy V.V.

Center for Expertise and Quality Control of Medical Care;
Russian Medical Academy of Continuous Professional Education;
Research Financial Institute;
Semashko National Research Institute of Public Health

ORCID: 0000-0003-1581-0703

Multiple linear regression analysis of cancer treatment effectiveness at the level of constituent entities in the Russian Federation

Authors:

Gorkavenko F.V., Shchurov D.G., Omelyanovskiy V.V.

More about the authors

Journal: Medical Technologies. Assessment and Choice. 2023;(1): 9‑26

DOI: 10.17116/medtech2023450119

Downloaded: 177

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To cite this article:

Gorkavenko FV, Shchurov DG, Omelyanovskiy VV. Multiple linear regression analysis of cancer treatment effectiveness at the level of constituent entities in the Russian Federation. Medical Technologies. Assessment and Choice. 2023;(1):9‑26. (In Russ.)
https://doi.org/10.17116/medtech2023450119

Подписка 2025 (Medical Technologies. Assessment and Choice)

Использование инфографики авторами научных работ

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OBJECTIVE

To improve cancer treatment at the level of constituent entities of the Russian Federation using machine learning technologies and analysis of prognostic factors correlating with quality of oncology care.

MATERIAL AND METHODS

We performed correlation and regression analysis of some potential predictors of one-year mortality from malignant neoplasms, crude and standardized mortality from neoplasms including malignant ones. A separate analysis was carried out for each year for the period from 2017 to 2020.

RESULTS

We constructed some predictive multiple linear regression models for one-year mortality with the highest R²=0.458, for the crude mortality rate — R²=0.182, and for the standardized mortality rate — R²=0.256. Early detection of cancer, increased prescripition of drug therapy for newly diagnosed cancers including combined therapies, targeted drug therapy, timely care and less incidence of isolated surgical treatment for newly diagnosed malignancies are necessary to improve the quality of oncology care at the level of constituent entities.

CONCLUSION

These data can be used in planning of oncological care at the level of constituent entities and future national and regional projects against cancers.

Keywords:

cancer treatment

oncology

malignant neoplasms

machine learning

linear regression

Authors:

Gorkavenko F.V.

Center for Expertise and Quality Control of Medical Care;
Russian Medical Academy of Continuous Professional Education

Scopus AuthorID: 57219030739
ORCID: 0000-0002-4586-2451

Shchurov D.G.

Center for Expertise and Quality Control of Medical Care

Scopus AuthorID: 57222291235
ORCID: 0000-0002-6703-4788

Omelyanovskiy V.V.

ORCID: 0000-0003-1581-0703

Received:

09.12.2022

Accepted:

26.12.2022

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