The Use of Tobit and Logistic Regression Models to Study Factors that Affect Blood Pressure in Cardiac Patients

Bekhal S. Sedeeq; Banaz W. Y. Meran

doi:10.24086/cuesj.v6n2y2022.pp133-140

Bekhal S. Sedeeq Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region, Iraq https://orcid.org/0000-0002-8243-8318
Banaz W. Y. Meran Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region, Iraq https://orcid.org/0000-0002-7147-2713

DOI: https://doi.org/10.24086/cuesj.v6n2y2022.pp133-140

Keywords: Akaike information criterion, Bayesian information criterion, censoring, logistic regression model, Tobit regression model, truncation

Abstract

This research witnessed using the Tobit and logistic regression models to study the factors that affect blood pressure in cardiac patients. The data have been collected, from 500 patients with heart disease in hospital-heart center - Erbil. The two levels of blood pressure, low and high blood pressures, were taken from the patients, as dependent variables and some, independent variables (Gender, Age, Urea, Cholesterol, Creatinine, and Weight). The researcher found that the median of blood pressure by means of arterial pressure (MAP) equation contains each of high and low blood pressures differently because the threshold point was determined to be (99.33), which is equal to,12/8 mmHg, which is the normal amount of blood pressure. The aim of this research is to explain the main concepts and processes of Tobit regression analysis (Censored- and Truncated) and logistic regression analysis, which is used for knowing which factors of independent variables has more effect on the response variables (blood pressure), and to compare the outcomes of the two models (Tobit regression and logistic regression) in order to determine which of the models best fits our data in which AIC and BIC are used. The researcher reached a conclusion that the logistic regression model best fits our data compared with Tobit regression, and it arrived at the results obtained by utilizing the statistical packages in R programming, MATLAB and Statistical Packages of Social Sciences (SPSS) V.26, which was used to analyze data from our research

Downloads

Download data is not yet available.

Author Biographies

Bekhal S. Sedeeq, Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region, Iraq

Bekhal S. Sedeeq is an Assistant prof. at the Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University. She got the B.Sc. degree in Statistics, the M.Sc. degree in Mathematical statistic, and the Ph.D. degree in Statistics from Salahaddin University. Her research interests are in Mathematical Statistics, Regression, Quality Control, Probability, and Reliability.

Banaz W. Y. Meran, Department of Statistics, College of Administration and Economics, Salahaddin University-Erbil, Kurdistan Region, Iraq

Banaz Walid Yaqoob Meran is a MSc student at the Department of Statistics and Informatics, College of Administration and Economics, Salahaddin University. She got a B.Sc. degree in Statistics. Her research interests are in Applied Statistics, Regression Model, Mathematical Statistics, Computer Application, Probability.

References

C. Y. Wu, H. Y. Hu, Y. J. Chou, N. Huang, Y. Chou and C. Li. High blood pressure and all-cause and cardiovascular disease mortalities in community-dwelling older adults. Medicine(Baltimore), vol. 94, no. 47, p. e2160, 2015.

W. H. Greene. Censored Data and Truncated Distributions, SSRN Electron. J, 2005.

M. H. Odah, B. K. Mohammed and A. S. M. Bager. Tobit regression model to determine the dividend yield in Iraq. LUMEN Proceedings, vol. 3, pp. 347-354, 2018.

J. S. Cramer. The Origins of Logistic Regression: Tinbergen Institute Discussion Papers, 2002.

C. M. Dayton. Logistic regression analysis. Stat, vol. 474, p. 574, 1992.

H. Shirafkan, J. Yazdani-Charati, S. A. Mozaffarpur, S. Khafri, R. Akbari and A. A. Pasha. Application of tobit model in time until Cytomegalovirus infection in kidney transplant recipients. Acta Medica, vol. 32, p. 1237, 2016.

R. M. H. Karim and S. M. Salh. Using tobit model for studying factors affecting blood pressure in patients with renal failure. UHD Journal of Science and Technology, vol. 4, no. 2, pp. 1-9, 2020.

H. R. Talib and S. A. Mazloum. The use of binary logistic regression method to analyze the factors affecting heart disease deaths: An applied study on a sample of patients in Dhi Qar Governorate. Journal of Al-Rafidain University College College for Sciences, 2020, no. 46, 2020.

N. Rambeli, E. Hashim, F. C. Leh, N. S. Hudin, M. F. Ramli, M. C. Mustafa, et al. Decision to leave or remain in the career as early childhood educator: A binary logistic regression model. Review of International Geographical Education Online, vol. 11, no. 5, pp. 450-456, 2021.

G. S. Maddala. Limited-Dependent and Qualitative Variables in Econometrics. New York, NY: Cambridge University, 1983.

N. M. Ahmed. Limited dependent variable modelling (truncated and censored regression models) with application. The Scientific Journal of Cihan University Sulaimanyia, vol. 2, no. 2, pp. 82-96, 2018.

T. Amemiya. Tobit models: A survey. Journal of Econometrics, vol. 24, no. (1-2), pp. 3-61, 1984.

J. S. Long, Regression Models for Categorical and Limited Dependent Variables. Thousand Oaks, CA: Saga Publication, 1997.

A. Flaih, J. Guardiola, H. Elsalloukh and C. Akmyradov. Statistical inference on the ESEP tobit regression model. Journal of Statistics Applications and Probability Letters, vol. 6, no. 1, pp. 1-9, 2019.

K. Y. Chay and J. L. Powell. Semiparametric censored regression models. Journal of Economic Perspectives, vol. 15, no. 4, pp. 29-42, 2001.

D. Hosmer Jr., S. Lemeshow and R. Sturdivant. Applied Logistic Regression. vol. 398. New York: John Wiley and Sons, 2013.

A. M. Khudhur and D. H. Kadir. An application of logistic regression modeling to predict risk factors for bypass graft diagnosis in Erbil. Cihan University-Erbil Scientific Journal, vol. 6, no. 1, pp. 57-63, 2022.

N. M. M. Abd Elsalam. Binary logistic regression to identify the risk factors of eye glaucoma. International Journal of Sciences Basic and Applied Research, vol. 23, no. 1, pp. 366-376, 2015.

N. S. K. Barznj. Using logistic regression analysis and linear discriminant analysis to identify the risk factors of diabetes. Zanco Journal of Humanity Sciences, vol. 22, no. 6, pp. 248-268, 2018.

S. Menard. Applied Logistic Regression Analysis. vol. 106, New York: Sage, 2002.

N. H. Mahmood, R. O. Yahya and S. J. Aziz. Apply binary logistic regression model to recognize the risk factors of diabetes through measuring glycated hemoglobin levels. Cihan University-Erbil Scientific Journal, vol. 6, no. 1, pp. 7-11, 2022.

V. Bewick, L. Cheek and J. Ball. Statistics review 14: Logistic regression. Critical Care, vol. 9, no. 1, pp.112-118, 2005.

D. Hosmer and S. Lemeshow. Applied Logistic Regression. New York: Johnson Wiley and Sons, 2000.

I. R. Soderstrom and D. W. Leitner. The effects of base rate, selection ratio, sample size, and reliability of predictors on predictive efficiency indices associated with logistic regression models, 1997.

S. Konishi and G. Kitagawa. Information Criteria and Statistical Modeling. Berlin: Springer Science and Business Media, 2008.

K. I. Mawlood. Using logistic regression and cox regression models to studying the most prognostic factors for leukemia patients. Qalaai Zanist Scientific Journal, vol. 4, no. 3, pp. 705- 724, 2019.