Comparative Analysis of Contingency Models in Medical Data using Log-linear Models and Logistic Regression Models

Laith Adnan  Hammad

Authors

Laith Adnan Hammad Open Educational College, Branch Samarra, Salah aL-Din Iraq

Keywords:

Tables Contingency, Log Model, Logistic Model, Weight

Abstract

This research explored the use of linear logistic studies and binary and multi-logarithmic reference models in analyzing n×m Tables Contingency for the development of relationships between genders. It also examined how each model interprets the relationships between these correlations. This research has applications in the medical field, where the effects of age and gender on weight were studied, and their impact on weight prediction was investigated. Logarithmic models identified weight gain in any age group, while logistic models developed a predictive equation for the effect of age and gender on weight. Each model has its own characteristics logarithmic models focus on studying the relationship between variables, while logistic models construct a predictive equation to interpret the results.

References

L. A. Goodman, “The Multivariate Analysis of Qualitative Data: Interactions among Multiple Classifications,” J. Am. Stat. Assoc., vol. 65, no. 329, pp. 226–256, 1970, doi: 10.1080/01621459.1970.10481076.

J. A. Nelder, “Log Linear Models for Contingency Tables: A Generalization of Classical Least Squares,” J. R. Stat. Soc. Ser. C (Applied Stat., vol. 23, no. 3, pp. 323–329, 1974, doi: 10.2307/2347125.

D. R. Cox, “The Regression Analysis of Binary Sequences,” J. R. Stat. Soc. Ser. B, vol. 20, no. 2, pp. 215–232, 1958, doi: 10.1111/j.2517-6161.1958.tb00292.x.

C. B. Begg and R. Gray, “Calculation of Polychotomous Logistic Regression Parameters Using Individualized Regressions,” Biometrika, vol. 71, no. 1, pp. 11–18, 1984, doi: 10.1093/biomet/71.1.11.

D. Böhning, “Multinomial Logistic Regression Algorithm,” Ann. Inst. Stat. Math., vol. 44, no. 1, pp. 197–200, 1992, doi: 10.1007/BF00048682.

S. S. Al-Obaidi and J. I. Al-Bayati, Applied Statistics: Methods of Hypothesis Testing, 3rd ed. Modern Academy for University Books, 2022.

GraphPad Software, “Contingency Table Analysis,” 2025, San Diego, California, USA.

S. J. Haberman, “Log-Linear Fit for Contingency Tables,” J. R. Stat. Soc. Ser. C (Applied Stat., vol. 21, no. 2, pp. 218–225, 1972, doi: 10.2307/2346506.

J. A. Nelder and R. W. M. Wedderburn, “Generalized Linear Models,” J. R. Stat. Soc. Ser. A, vol. 135, no. 3, pp. 370–384, 1972, doi: 10.2307/2344614.

V. Bewick, L. Cheek, and J. Ball, “Statistics Review 14: Logistic Regression,” Crit. Care, vol. 9, no. 1, pp. 112–118, 2005, doi: 10.1186/cc3045.

J. C. Stoltzfus, “Logistic Regression: A Brief Primer,” Acad. Emerg. Med., vol. 18, no. 10, pp. 1099–1104, 2011, doi: 10.1111/j.1553-2712.2011.01185.x.

J. Fox, “The {R} Commander: A Basic-Statistics Graphical User Interface to {R},” J. Stat. Softw., vol. 14, no. 9, pp. 1–42, 2005, doi: 10.18637/jss.v014.i09.

S. H. A. Al-Farhud, “Using Logistic Regression to Study the Factors Affecting Stock Performance,” J. Al-Azhar Univ. (Natural Sci. Ser., vol. 9, pp. 47–68, 2014.

T. A. Al-Khazaali, “Estimation of a Logistic Regression Model for a Multi-Level Dependent Variable,” University of Karbala, Iraq, 2021.

I. S. Hanaish and I. I. Al-Daq, “Using Binary Response Logistic Regression Technique to Diagnose the Most Important Factors Affecting the Causes of Premature Infant Mortality: A Case Study in the City of Misrata for the Years 2019 and 2022,” Afaq Al-Ma’rifah J., vol. 2, no. 8, p. 21, 2025.