The Importance of Hierarchical Regression in Public Health Data Modeling

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DOI:

https://doi.org/10.54103/2282-0930/29967

Keywords:

Hierarchical Regression, Public Health, Outcome Prediction

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Author Biographies

Farzan Madadizadeh, Shahid Sadoughi University of Medical Sciences and Health Services

Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health

Hossein Akhondi, Shahid Sadoughi University of Medical Sciences and Health Services

Center for Healthcare Data Modeling, Departments of Biostatistics and Epidemiology, School of Public Health

References

Ross, A., V.L. Willson, A. Ross, and V.L. Willson, Hierarchical multiple regression analysis using at least two sets of variables (in two blocks). Basic and Advanced Statistical Tests: Writing Results Sections and Creating Tables and Figures, 2017: p. 61-74.

Lobo, M. and S.L. Normand, Hierarchical models in health services research. Wiley StatsRef: Statistics Reference Online, 2014: p. 1-7.

Dwivedi, S.N., S. Begum, A.K. Dwived, and A. Pandey, Community effects on public health in India: A hierarchical model. Health, 2012. 4(8): p. 526-536.

Gatsonis, C. Aspects of hierarchical regression modeling in health services and outcomes research. in Proceedings of the 23rd International Conference on Information Technology Interfaces, 2001. ITI 2001. 2001. IEEE.

Cohen, J., P. Cohen, S.G. West, and L.S. Aiken, Applied multiple regression/correlation analysis for the behavioral sciences. 2013: Routledge.

Mahajan, K.C., A. Suryawanshi, S. Shendage, S. Agawane, A. Kurhade, and H. Bhor, A Comprehensive Review of Various Factors Impacting Human Health: Implications for Pharmaceutical Education.

Pretorius, T.B., Pathways to health: Conceptual clarification and appropriate statistical treatment of mediator, moderator, and indirect effects using examples from burnout research. South African Journal of Psychology, 2020. 50(3): p. 320-335.

Młynarczyk, D., C. Armero, V. Gómez-Rubio, and P. Puig, Bayesian analysis of population health data. Mathematics, 2021. 9(5): p. 577.

Richardson, D.B., R.F. MacLehose, B. Langholz, and S.R. Cole, Hierarchical latency models for dose-timeresponse associations. American journal of epidemiology, 2011. 173(6): p. 695-702.

Currie, D.J., C. Smith, and P. Jagals, The application of system dynamics modelling to environmental health decision-making and policy-a scoping review. BMC public health, 2018. 18: p. 1-11.

Richardson, D.B., G.B. Hamra, R.F. MacLehose, S.R. Cole, and H. Chu, Hierarchical regression for analyses of multiple outcomes. American journal of epidemiology, 2015. 182(5): p. 459-467.

De Roos, A.J., C. Poole, K. Teschke, and A.F. Olshan, An application of hierarchical regression in the investigation of multiple paternal occupational exposures and neuroblastoma in offspring. American journal of industrial medicine, 2001. 39(5): p. 477-486.

Gelman, A. and J. Hill, Data analysis using regression and multilevel/hierarchical models. 2007: Cambridge university press.

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Published

2025-11-07

How to Cite

1.
Madadizadeh F, Akhondi H. The Importance of Hierarchical Regression in Public Health Data Modeling. ebph [Internet]. 2025 [cited 2026 Feb. 5];20(2). Available from: https://riviste.unimi.it/index.php/ebph/article/view/29967

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