Logistic Regression in Nursing Practice
Logistic regression is used to analyze a wide variety of variables that may surround a singular outcome. For example, logistic regression could be used to identify the likelihood of a patient having a heart attack or stroke based on a variety of factors including age, sex, genetic characteristics, weight, and any preexisting health conditions. The biological systems and issues with which the health care field is concerned represent the kinds of applications for which logistic regression is especially useful.
Logistic regression is used in the health care field for many purposes, including diagnoses, predictions, and forecasting. The three articles in this week’s Learning Resources illustrate the many uses of logistic regression in the health care field. This Discussion allows you to explore the different uses of logistic regression and cultivate a deeper understanding of the application of logistic regression in evidence-based practice.
By Thursday 10/12/17, 5 pm, write a minimum of 550 words essay in APA format. Use at least 3 references from the list of required reading below. Include the level one headings as numbered below.
Post a cohesive response that addresses the following:
1) In the first line of your posting, identify the article you examined, providing its correct APA citation. (See attached PDF file for the article).
2) Post your critical analysis of the article as outlined above (make sure to answer all the points asked above in the area, bullets [1, 2, 3]).
3) Propose potential remedies to address the weaknesses of each study (bullets 4 and 5 in the area).
4) Analyze the importance of this study to evidence-based practice, the nursing profession, or society (bullet 6 in the area).
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Used by permission from SPSSVideoTutor.com A division of Consumer Raters LLC., 1121 S Military Trail, 314, Deerfield Beach, FL 33442, USA
Note: The approximate length of this media piece is 15 minutes.
Gray, J.R., Grove, S.K., & Sutherland, S. (2017). Burns and Grove’s the practice of nursing research: Appraisal, synthesis, and generation of evidence (8th ed.). St. Louis, MO: Saunders Elsevier.
Chapter 24, “Using Statistics to Predict”
This chapter asserts that predictive analyses are based on probability theory instead of decision theory. It also analyzes how variation plays a critical role in simple linear regression and multiple regression.
Statistics and Data Analysis for Nursing Research
Chapter 9, “Correlation and Simple Regression” (pp. 208–222)
This section of Chapter 9 discusses the simple regression equation and outlines major components of regression, including errors of prediction, residuals, OLS regression, and ordinary least-square regression.
Chapter 10, “Multiple Regression”
Chapter 10 focuses on multiple regression as a statistical procedure and explains multivariate statistics and their relationship to multiple regression concepts, equations, and tests.
Chapter 12, “Logistic Regression”
This chapter provides an overview of logistic regression, which is a form of statistical analysis frequently used in nursing research.
Hoerster, K. D., Mayer, J. A., Gabbard, S., Kronick, R. G., Roesch, S. C., Malcarne, V. L., & Zuniga, M. L. (2011). Impact of individual-, environmental-, and policy-level factors on health care utilization among US farmworkers. American Journal of Public Health, 101(4), 685–692. doi:10.2105/AJPH.2009.190892
This article discusses the results of a study of how many U.S. farmworkers accessed U.S. health care. The study considered this question on several levels—individual, environmental, and policy—and used logistic regression to analyze the multivariate data gathered.
Tritica-Majnaric, L., Zekic-Susac, M., Sarlija, N., & Vitale, B. (2010). Prediction of influenza vaccination outcome by neural networks and logistic regression. Journal of Biomedical Informatics, 43(5), 774–781. doi:10.1016/j.jbi.2010.04.011.
This article describes the methods and results of a neural network study on the effectiveness of the influenza vaccine using historical data in three neural network algorithms. The article also provides a discussion of logistic regression in comparison to the neural network algorithms used.
Xiao, Y., Griffin, M. P., Lake, D. E., & Moorman, J. R. (2010). Nearest-neighbor and logistic regression analyses of clinical and heart rate characteristics in the early diagnosis of neonatal sepsis. Medical Decision Making, 30(2), 258–266. doi:10.1177/0272989X09337791
This article outlines the procedures and findings of a study on the use of two methods of neonatal sepsis diagnosis: nearest-neighbor analysis and logistic regression analysis. The results indicated that each method generates unique information useful to diagnosis, and therefore both methods should be used simultaneously for improved accuracy of diagnoses.