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Regression analysis is a fundamental statistical method widely employed in clinical research to explore the quantitative relationships between exposure variables and various outcomes. By using regression models, researchers can identify key associations between predictors and outcomes, which is essential for making data-driven clinical decisions. The main types of regression models include linear regression, logistic regression, Cox proportional hazards regression, Poisson regression, and negative binomial regression. These models are tailored to different types of dependent variables, such as continuous outcomes, binary outcomes, survival times, and event counts. Each regression model has its own assumptions and limitations, which must be carefully considered to ensure valid results. This study provides an in-depth overview of these regression models, their application characteristics, and common assumptions in clinical research. Additionally, it evaluates their potential contributions to precision medicine and discusses the challenges and solutions in clinical practice, aiming to provide theoretical and application reference for clinical researchers and biostatisticians.
Regression analysis; Clinical research; Regression models
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The Application and Challenges of Regression Analysis Methods in Clinical Research
How to cite this paper: Haicheng Fei, Hongying Jia. (2025) The Application and Challenges of Regression Analysis Methods in Clinical Research. International Journal of Clinical and Experimental Medicine Research, 9(6), 680-683.
DOI: http://dx.doi.org/10.26855/ijcemr.2025.11.019