Clinical Risk Prediction with Logistic Regression: Best Practices, Validation Techniques, and Applications in Medical Research

Objectives This narrative review aims to provide a comprehensive and clinically relevant synthesis of logistic regression applications in clinical medicine, particularly in risk prediction and diagnostic modeling. Key objectives include evaluating best practices, addressing common pitfalls, and outlining validation techniques when using logistic regression to analyze binary outcomes such as disease presence versus absence. Methods The review synthesizes data from 41 peer-reviewed articles spanning from 1987 to 2025, selected from databases including PubMed, MEDLINE, and Scopus using keywords including “logistic regression,” “clinical medicine,” “diagnostic studies,” “prognostic models,” “odds ratio,” and “model validation.” The narrative approach was chosen to integrate findings from various study designs, allowing for a broad discussion on the advantages and limitations of logistic regression in clinical research. The manuscript details key methodological considerations such as the appropriate coding of continuous and categorical variables, verification of core assumptions (e.g., linearity in the log-odds, independence of observations, absence of perfect separation), and adherence to sample size requirements. In addition, the review highlights the importance of splitting datasets into training, validation, and testing subsets, and incorporates performance metrics including sensitivity, specificity, precision, and F1 scores. Results The review reveals that logistic regression remains a cornerstone technique in clinical risk prediction due to its interpretability and robust framework for handling binary outcomes. Findings indicate that logistic regression models, when appropriately validated, significantly enhance diagnostic accuracy and provide reliable risk estimates through odds ratios and confidence intervals. The review identifies that data integrity, proper variable categorization, and rigorous assumption checks are critical for avoiding model misclassification. Furthermore, visual tools like violin plots are highlighted for their utility in comparing distributions of predicted probabilities across different outcome groups. Real-world examples demonstrate that factors such as biomarker levels (e.g., troponin in acute coronary syndrome) and patient characteristics (e.g., albumin levels, BMI in postoperative infections) are effectively modeled using logistic regression, leading to clinically meaningful inferences. Conclusion Logistic regression is an indispensable tool in clinical research for predicting binary outcomes and informing evidence-based practice. By integrating a detailed discussion of best practices, common pitfalls, and model validation techniques, the manuscript offers a definitive guide for clinicians and researchers. It emphasizes that rigorous adherence to methodological standards—from data preparation to performance evaluation—can significantly improve predictive accuracy and clinical decision-making. This study hopes to serve as a valuable reference to clinicians, and explain statistical and machine learning topics in a clinical context that is easily understood and widely accessible.