Primer on binary logistic regression
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- 1 Brown School, Washington University in St Louis, St Louis, Missouri, USA [email protected].
- PMID: 34952854
- PMCID: PMC8710907
- DOI: 10.1136/fmch-2021-001290
Family medicine has traditionally prioritised patient care over research. However, recent recommendations to strengthen family medicine include calls to focus more on research including improving research methods used in the field. Binary logistic regression is one method frequently used in family medicine research to classify, explain or predict the values of some characteristic, behaviour or outcome. The binary logistic regression model relies on assumptions including independent observations, no perfect multicollinearity and linearity. The model produces ORs, which suggest increased, decreased or no change in odds of being in one category of the outcome with an increase in the value of the predictor. Model significance quantifies whether the model is better than the baseline value (ie, the percentage of people with the outcome) at explaining or predicting whether the observed cases in the data set have the outcome. One model fit measure is the count- [Formula: see text], which is the percentage of observations where the model correctly predicted the outcome variable value. Related to the count- [Formula: see text] are model sensitivity-the percentage of those with the outcome who were correctly predicted to have the outcome-and specificity-the percentage of those without the outcome who were correctly predicted to not have the outcome. Complete model reporting for binary logistic regression includes descriptive statistics, a statement on whether assumptions were checked and met, ORs and CIs for each predictor, overall model significance and overall model fit.
Keywords: education; epidemiology; public health.
© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.
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International Conference on Intelligent Computing & Optimization
ICO 2023: Intelligent Computing and Optimization pp 56–64 Cite as
K-Modes with Binary Logistic Regression: An Application in Marketing Research
- Jonathan Rebolledo 16 &
- Roman Rodriguez-Aguilar 17
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- First Online: 20 December 2023
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Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 855))
Binary logistic regression is a statistical method used to analyze data with binary outcome variables. It is a type of generalized linear model (GLM) and is often used in fields such as medicine, psychology, and sociology. Using this model with some degree of data noise might give wrong results.
- Logistic regression
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Agresti, A.: Categorical data analysis (3rd ed.). John Wiley & Sons (2013)
Google Scholar
Bishop, C.M. (ed.): Pattern Recognition and Machine Learning. ISS, Springer, New York (2006). https://doi.org/10.1007/978-0-387-45528-0
Book Google Scholar
DeSarbo, W.S., Ramaswamy, V., Cohen, S.H.: Market segmentation with choice-based conjoint analysis. Mark. Lett. 6 (2), 137–147 (1995)
Article Google Scholar
Everitt, B.S., Hothorn, T.: An introduction to applied multivariate analysis with R. Springer (2011)
Field, A.: Discovering statistics using IBM SPSS statistics (5th ed.). SAGE Publications (2018)
Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E.: Multivariate data analysis (8th ed.). Cengage Learning (2018)
Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: Data mining, inference, and prediction (2nd ed.). Springer (2009)
Hosmer, D. W., Lemeshow, S., & Sturdivant, R.X.: Applied logistic regression (3rd ed.). John Wiley & Sons (2013)
Huber, P.J.: Robust Statistics. John Wiley & Sons (1981)
Huang, Z.: A fast-clustering algorithm to cluster very large categorical data sets in data mining. Res. Issues Data Min. Knowl. Discov. (DMKD ‘97) (1997)
James, G., Witten, D., Hastie, T., Tibshirani, R.: An introduction to statistical learning: with applications in R. Springer (2013)
Jolliffe, I.T., Cadima, J.: Principal component analysis: a review and recent developments. Philos. Trans. Royal Soc. A: Math., Phy. Eng. Sci. 374 (2065), 20150202 (2016)
Article MathSciNet Google Scholar
Kelleher, J.D., Mac Namee, B., D’Arcy, A.: Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. MIT Press (2015)
Malhotra, N.K.: Marketing research: an applied orientation (7th ed.). Pearson (2019)
McCullagh, P., Nelder, J.A.: Generalized linear models (2nd ed.). Chapman and Hall/CRC (1989)
Tibshirani, R.: Regression shrinkage and selection via the Lasso. J. Roy. Stat. Soc.: Ser. B (Methodol.) 58 (1), 267–288 (1996)
MathSciNet Google Scholar
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Facultad de Ingeniería, Universidad Anáhuac México, Av. Universidad Anahuac 46, Lomas Anahuac, 52786, Lomas Anahuac, Mexico
Jonathan Rebolledo
Facultad de Ciencias Económicas y Empresariales, Universidad Panamericana, Augusto Rodin 498, 03920, Mexico City, Mexico
Roman Rodriguez-Aguilar
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Pandian Vasant
Department of Computer Science, Chittagong University of Engineering & Technology, Chittagong, Bangladesh
Mohammad Shamsul Arefin
Federal Scientific Agroengineering Center VIM, Laboratory of Non-traditional Energy Systems, Russian University of Transport, Department of Theoretical and Applied Mechanics, 127994 Moscow, Russia;, Moscow, Russia
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Northwest University, Mmabatho, South Africa
Elias Munapo
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Gerhard-Wilhelm Weber
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Rebolledo, J., Rodriguez-Aguilar, R. (2023). K-Modes with Binary Logistic Regression: An Application in Marketing Research. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 855. Springer, Cham. https://doi.org/10.1007/978-3-031-50158-6_6
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Using Logistic Regression Model to Study the Most Important Factors Which Affects Diabetes for The Elderly in The City of Hilla / 2019
Zainab Abood Ahmed Al_Bairmani 1 and Aasha Abdulkhleq Ismael 1
Published under licence by IOP Publishing Ltd Journal of Physics: Conference Series , Volume 1818 , Iraqi Academics Syndicate International Conference for Pure and Applied Sciences (IICPS), 5-6 December 2020, Babylon, Iraq Citation Zainab Abood Ahmed Al_Bairmani and Aasha Abdulkhleq Ismael 2021 J. Phys.: Conf. Ser. 1818 012016 DOI 10.1088/1742-6596/1818/1/012016
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1 University of Babylon, Dep. Quality Assurance & Academic Performance
2 Tikrit University - College of Administration & Economics
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The aim of this paper is to study the most important factors affecting diabetes using the logistic regression method and to conduct all tests for this method (Hosmer and Lemeshow test, Omnibus tests of model coefficients, ...etc.). The randomized sample included (150) people among the elderly in Al-Hilla city, the research included focusing on (14) independent variables and most of these variables were found to have significance, effect and contribution to the logistic regression - binary response (not sick(0), sick(1)) model are (4) variables (cigarette smoking, exercise, vitamin (D), blood pressure), Which affects diabetes, and the rest of the variables have no significance or effect. The classification of observations using logistic regression-binary response model was accurate, as the overall correct classification rate was (92.7%) while the overall wrong classification rate was (7.3%).
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Ordinal Logistic Regression in Medical Research
Ralf bender.
Statistician, Department of Metabolic Diseases and Nutrition, Heinrich-Heine-University of Düsseldorf
Ulrich Grouven
Statistician, Department of Anaesthesiology, Hannover Medical School
Medical research workers are making increasing use of logistic regression analysis for binary and ordinal data. The purpose of this paper is to give a non-technical introduction to logistic regression models for ordinal response variables. We address issues such as the global concept and interpetation of logistic models, the model building procedure from a practical point of view, and the assessment of the model adequacy. For illustrative purposes we apply these methods to real data of a study investigating the association between glycosylated haemoglobin and retinopathy. We give some recommendations for the use and assessment of ordinal logistic regression models in medical research.
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