Abstract
This study provides an in-depth examination of the critical role that survival analysis plays across various disciplines, with a particular emphasis on its applications in clinical research, public policy, and economics. The research is designed to offer a comprehensive overview of traditional survival analysis methods, highlight emerging trends, explore practical applications, and discuss the challenges and future directions for the field. By conducting a thorough review of existing literature and analyzing contemporary advancements, this study employs a robust and methodical approach. Key findings suggest that traditional survival analysis techniques, such as the Kaplan-Meier estimator and the Cox (1072) proportional hazards model, continue to serve as foundational tools in the field. However, the integration of big data and advanced computational technologies has significantly enhanced the precision and broadened the applicability of survival analysis, facilitating more accurate predictions and wider applications. In clinical research, survival analysis remains indispensable for assessing patient outcomes and guiding treatment decisions. Furthermore, the study highlights the growing importance of cross-disciplinary collaborations, which are increasingly essential for addressing both ethical and methodological challenges, thereby enhancing the utility of survival analysis across various sectors. The study concludes that while traditional methods retain their relevance, the future of survival analysis will be shaped by the integration of modern computational tools and the promotion of cross-disciplinary collaborations. Recommendations include further exploration of machine learning and artificial intelligence within survival analysis, as well as encouraging collaborative efforts across diverse fields to address current challenges and expand the application of survival analysis in new and innovative ways.
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