A Review on Logistic Regression in Medical Research

Authors

  • Nihar Ranjan Panda Biostatistician

DOI:

https://doi.org/10.55489/njcm.134202222

Keywords:

Logistic regression, Odds ratio, R programming

Abstract

In today’s scenarios many healthcare decisions are being taken by predictive modeling and machine learning techniques. With this review, we focused on logistic regression model, a kind of predictive modeling used in machine learning, and how healthcare researchers take decisions by the help of predictive modeling. For a better data analysis in healthcare, we need to understand the concept of logistic regression as well as others terms, which are linked with it. so that we can clearly understand the concept behind it and implement in medical research. In this review we worked on an example and illustrated how to perform logistic regression using R programming language. The aim of this paper is to understand logistic regression in healthcare and implement it for decision making.

References

Oommen, T., Baise, L.G. and Vogel, R.M. (2011) Sampling Bias and Class Imbalance in Maximum-Likelihood Logistic Regres-sion. Mathematical Geosciences, 43, 99-120. https://doi.org/10.1007/s11004-010-9311-8

Cramer, J.S. (2002) The Origins of Logistic Regression. Tin-bergen Institute Working Paper.

Tu, J.V. (1996) Advantages and Disadvantages of Using Artifi-cial Neural Networks versus Logistic Regression for Predict-ing Medical Outcomes. Journal of Clinical Epidemiology, 49, 1225-1231. https://doi.org/10.1016/S0895-4356(96)00002-9

Hosmer D.W. and Lemeshow, S. (2000) Applied Logistic Re-gression. 2nd Edition, Wiley, New York. https://doi.org/10.1002/0471722146 https://onlinelibrary.wiley.com/doi/book/10.1002/0471722146

King, G. and Zeng, L. (2001) Logistic Regression in Rare Events Data. Political Analysis, 9, 137-163. https://doi.org/10.1093/oxfordjournals.pan.a004868

Hosmer, D.W. and Lemeshow, S. (1989) Applied Logistic Re-gression. John Wiley & Sons, New York.

Bacaër, N. (2011) Verhulst and the Logistic Equation. In: A Short History of Mathematical Population Dynamics, Spring-er, London, 35-39. https://doi.org/10.1007/978-0-85729-115-8_6

Pearl, R. and Reed, L.J. (1920) On the Rate of Growth of the Population of the United States since 1790 and Its Mathemati-cal Representation. Proceedings of the National Academy of Sciences of the United States of America, 6, 275-288. https://doi.org/10.1073/pnas.6.6.275

Boateng, E.Y. and Oduro, F.T. (2018) Predicting Microfinance Credit Default: A Study of Nsoatreman Rural Bank Ghana. Journal of Advances in Mathematics and Computer Science, 26, 1-9. https://doi.org/10.9734/JAMCS/2018/33569

Hosmer, D.W., Lemeshow, S. and Sturdivant, R.X. (1989) The Multiple Logistic Regression Model. Applied Logistic Regres-sion, 1, 25-37.

Srivastava, N. (2005) A Logistic Regression Model for Pre-dicting the Occurrence of Intense Geomagnetic Storms. An-nales Geophysicae, 23, 2969-2974. https://doi.org/10.5194/angeo-23-2969-2005

Khan, K.S., Chien, P.F. and Dwarakanath, L.S. (1999) Logistic Regression Models in Obstetrics and Gynecology Literature. Obstetrics & Gynecology, 93, 1014-1020. https://doi.org/10.1097/00006250-199906000-00024 https://www.ncbi.nlm.nih.gov/pubmed/10362173

Kim, Y., Kwon, S. and Song, S.H. (2006) Multiclass Sparse Lo-gistic Regression for Classification of Multiple Cancer Types Using Gene Expression Data. Computational Statistics & Data Analysis, 51, 1643-1655. https://doi.org/10.1016/j.csda.2006.06.007

Jones, S.R. and McEwen, M.K. (2000) A Conceptual Model of Multiple Dimensions of Identity. Journal of College Student Development, 41, 405-414. https://www.researchgate.net/publication/292759031_A_conceptual_model_of_multiple_dimensions_of_identity

Vollmer, R.T. (1996) Multivariate Statistical Analysis for Pathologists: Part I, The Logistic Model. American Journal of Clinical Pathology, 105, 115-126.

Kabacoff R. R in action. Cherry Hill: Manning Publications Co; 2011.

Bendal RB, Afifi AA. Comparison of stopping rules in forward regression. Journal of the American Statistical Association 1977;72:46-53.

Mickey RM, Greenland S. The impact of confounder selection criteria on effect estimation. Am J Epidemiol 1989;129:125-37

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Published

2022-04-30

How to Cite

1.
Panda NR. A Review on Logistic Regression in Medical Research. Natl J Community Med [Internet]. 2022 Apr. 30 [cited 2024 Mar. 29];13(04):265-70. Available from: https://www.njcmindia.com/index.php/file/article/view/22

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Section

Review Articles