Методы оценки суммарного влияния множества факторов на проявление эффекта в медико-биологических исследованиях

В медико-биологических исследованиях часто требуется количественно оценить совокупное воздействие множества факторов - как количественных, так и качественных - на определённый биомедицинский эффект. В данной статье рассматриваются подходы к нормализации данных, определению весов факторов, агрегированию информации и построению моделей для оценки суммарного влияния факторов. Обсуждаются как экспертные, так и основанные на данных методы, а также их применение в задачах прогнозирования состояния здоровья, оценки риска и принятия решений в биомедицине.

1. Zhang X., Lee J., Goh W. W. B. An investigation of how normalisation and local modelling techniques confound machine learning performance in a mental health study // Heliyon. – 2022. – Т.8. – №. 5. https://scholar.google.com/scholar?output=instlink&q=info:RV1mT0muf8kJ:scholar.google.com/&hl=ru&as_sdt=0,5&scillfp=9109182181854836428&oi=lle

2. Salomon V. A. P., Gomes L. F. A. M. Consistency improvement in the analytic hierarchy process // Mathematics. – 2024. – Т.12. – №.6. – С. 828. https://www.mdpi.com/2227-7390/12/6/828/pdf

3. Best R. W. et al. An Overview of the Delphi Method’s Origin, Modifications, and Use to Augment Instrument Development and Data Collection: A Research Note // Journal of International Agricultural and Extension Education. – 2025. – Т.32. – №.1. – С.4. https://newprairiepress.org/cgi/viewcontent.cgi?article=1497&context=jiaee

4. Dey D. et al. The proper application of logistic regression model in complex survey data: a systematic review // BMC Medical Research Methodology. – 2025. – Т.25. – №.1. – С.15. https://link.springer.com/content/pdf/10.1186/s12874-024-02454-5.pdf

5. Johnson R. A. quantile-forest: A python package for quantile regression forests // Journal of Open Source Software. – 2024. – Т.9. – №.93. – С.5976. https://joss.theoj.org/papers/10.21105/joss.05976.pdf

6. Tibshirani R. Regression shrinkage and selection via the lasso // Journal of the Royal Statistical Society Series B: Statistical Methodology. – 1996. – Т.58. – №.1. – С.267-288. https://www.ccs.neu.edu/home/eelhami/courses/EE290A/LASSO_Tibshirani.pdf

7. Keeney R. L., Raiffa H. Decisions with multiple objectives: preferences and value trade-offs. – Cambridge university press, 1993. https://books.google.com/books?hl=ru&lr=&id=1oEa-BiARWUC&oi=fnd&pg=PR11&dq=7.%09Keeney+R.+L.,+Raiffa+H.+Decisions+with+multiple+objectives:+preferences+and+value+trade-offs.+–+Cambridge+university+press,+1993.&ots=cEDKV0sn-x&sig=6ZuyQg2d9m_xLb6dPAg45zN5F-Y

8. Bishop C. M., Nasrabadi N. M. Pattern recognition and machine learning. – New York : springer, 2006. – Т. 4. – №. 4. – С. 738. http://crowleycoutaz.fr/jlc/Courses/2020/PRML/ENSI3.PRML.S6.Encoders.pdf

9. Todaro S. et al. Correlation between extinction pattern and δ13C fluctuations across the Triassic Jurassic boundary in shallow water settings: a proxy for the present day acidification processes // "Geosciences for the environment, natural hazard and cultural heritage" Congresso SGI-SIMP 2018-Abstract book. – https://www.socgeol.it/files/download/pubblicazioni/AbstractBook/AbstractCatania_ok.pdf, 2018. – С. 20- 20. https://iris.unipa.it/handle/10447/298218

10. Bedrick E. J. Data Reduction Prior to Inference: Are There Consequences of Comparing Groups Using at-Test Based on Principal Component Scores? // Biometrics. – 2020. – Т.76. – №.2. – С.508-517. https://academic.oup.com/biometrics/article-abstract/76/2/508/7452939

11. James G. et al. An introduction to statistical learning. – New York : springer, 2013. – Т.112. – №.1. https://thuvienso.hoasen.edu.vn/bitstream/handle/123456789/10495/Contents.pdf?sequence=1

12. Tibshirani R. J., Efron B. An introduction to the bootstrap //Monographs on statistics and applied probability. – 1993. – Т.57. – №.1. – С.1-436. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=5765871610321f83521c283980e47755fe793d1c

13. Lundberg S. M., Lee S. I. A unified approach to interpreting model predicttions // Advances in neural information processing systems. – 2017. – Т. 30. https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf