[A review of correlation, regression and multiple factors analysis methods.].

Area of Science:

• Clinical Biostatistics
• Epidemiological Research Methods

Context:

• Clinical research frequently investigates relationships between variables.
• Understanding dependence and covariation is crucial for interpreting study findings.
• Multiple analysis methods offer enhanced specificity compared to single-factor approaches.

Purpose:

• To introduce fundamental principles and clinical utilities of statistical methods.
• To guide clinicians and researchers in selecting appropriate data analysis techniques.
• To cover key methods including linear correlation, linear regression, rank regression, rank correlation, multiple linear regression, and logistic regression.

Summary:

• This review details statistical methods for analyzing relationships in clinical research.
• Key techniques discussed include various regression and correlation analyses.
• The paper emphasizes the importance of choosing the correct statistical method for robust data interpretation.

Impact:

• Enhances the quality of clinical research through appropriate statistical application.
• Facilitates more precise interpretation of factor influences in disease and treatment studies.
• Aims to improve data collection and analysis strategies for better research outcomes.