Survival analysis is a fundamental tool in clinical research for evaluating time-to-event outcomes. While the Kaplan-Meier method remains a widely used univariable approach for estimating survival probabilities and comparing groups, it does not account for multiple risk factors simultaneously. To address this limitation, multivariable regression models are employed, with the Cox proportional hazards model (Cox regression) being the most commonly used. This paper provides a practical guide to Cox regression for clinicians, emphasizing its application in survival analysis rather than focusing on mathematical derivations. We discuss key concepts, including hazard ratios, model assumptions, variable selection, and interpretation of results. Additionally, we explore essential methodological considerations, such as assessing proportional hazards assumptions, handling missing data, and avoiding overfitting. By offering a step-by-step approach to implementing Cox regression in clinical research, this article aims to enhance understanding and improve the quality of survival analysis in medical studies. Practical examples illustrate how to interpret Cox regression results and their relevance in clinical decision-making.
Gomes et al. (2026) studied this question.