Methods for assessing the cumulative effect of multiple factors on the manifestation of the effect in biomedical research
Abstract
In biomedical research, it is often necessary to quantitatively assess the combined effect of multiple factors-both quantitative and qualitative-on a specific biomedical outcome. This article discusses approaches to data normalization, determination of factor weights, information aggregation, and model development for evaluating the cumulative influence of factors. Both expert-based and data-driven methods are examined, along with their application to health prediction, risk assessment, and decision-making in biomedicine.
About the Authors
Z. B. SabirovKazakhstan
PhD, Head of the Scientific Laboratory of Occupational Pathology
100017, Karaganda, Mustafina str. 15, e-mail: info@naoncgt.kz
E. Z. Otarov
Kazakhstan
100017, Karaganda, Mustafina str. 15, e-mail: info@naoncgt.kz
A. V. Alekseev
Kazakhstan
100017, Karaganda, Mustafina str. 15, e-mail: info@naoncgt.kz
O. V. Grebeneva
Kazakhstan
100017, Karaganda, Mustafina str. 15, e-mail: info@naoncgt.kz
A. Z. Shadetova
Kazakhstan
100017, Karaganda, Mustafina str. 15, e-mail: info@naoncgt.kz
References
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
Review
For citations:
Sabirov Z.B., Otarov E.Z., Alekseev A.V., Grebeneva O.V., Shadetova A.Z. Methods for assessing the cumulative effect of multiple factors on the manifestation of the effect in biomedical research. Occupational Hygiene and Medical Ecology. 2025;(3):9-17.