Survival analysis is a statistical method used to analyze time-to-event data, such as time until death or recurrence in cancer studies. The main challenge in survival analysis is that not all individuals experience the event of interest by the end of the study, leading to censored data. Survival times are often skewed and not normally distributed, necessitating specialized methods. This paper introduces the basic concepts of survival analysis, including the use of Kaplan-Meier (KM) plots, logrank tests, and Cox regression. It also discusses other approaches to survival analysis and their applications in cancer studies. The paper explains the concept of survival time, which can refer to time from diagnosis to death or from remission to relapse. It highlights the importance of defining the event of interest and the period of observation. Censoring, where some individuals do not experience the event by the end of the study, is a key aspect of survival analysis. There are different types of censoring, including right, left, and interval censoring. The paper provides examples of survival analysis in ovarian and lung cancer studies, illustrating how survival curves are constructed and interpreted. It discusses the survival function, hazard function, and cumulative hazard function, which are essential for understanding survival data. The logrank test is used to compare survival curves between groups, and the hazard ratio (HR) is a measure of the relative survival experience between groups. The paper also addresses key requirements for survival analysis, including uninformative censoring, sufficient follow-up time, and completeness of follow-up. It emphasizes the importance of adjusting for patient-related factors (covariates) when comparing treatments or prognostic groups. Multivariate survival analysis is a form of multiple regression used to adjust for these factors. In conclusion, survival analysis is a critical tool in cancer research for understanding time-to-event outcomes. It involves specialized statistical methods to handle censored data and provide meaningful insights into survival experiences. The paper outlines the basic concepts and methods of survival analysis, emphasizing their importance in cancer studies.Survival analysis is a statistical method used to analyze time-to-event data, such as time until death or recurrence in cancer studies. The main challenge in survival analysis is that not all individuals experience the event of interest by the end of the study, leading to censored data. Survival times are often skewed and not normally distributed, necessitating specialized methods. This paper introduces the basic concepts of survival analysis, including the use of Kaplan-Meier (KM) plots, logrank tests, and Cox regression. It also discusses other approaches to survival analysis and their applications in cancer studies. The paper explains the concept of survival time, which can refer to time from diagnosis to death or from remission to relapse. It highlights the importance of defining the event of interest and the period of observation. Censoring, where some individuals do not experience the event by the end of the study, is a key aspect of survival analysis. There are different types of censoring, including right, left, and interval censoring. The paper provides examples of survival analysis in ovarian and lung cancer studies, illustrating how survival curves are constructed and interpreted. It discusses the survival function, hazard function, and cumulative hazard function, which are essential for understanding survival data. The logrank test is used to compare survival curves between groups, and the hazard ratio (HR) is a measure of the relative survival experience between groups. The paper also addresses key requirements for survival analysis, including uninformative censoring, sufficient follow-up time, and completeness of follow-up. It emphasizes the importance of adjusting for patient-related factors (covariates) when comparing treatments or prognostic groups. Multivariate survival analysis is a form of multiple regression used to adjust for these factors. In conclusion, survival analysis is a critical tool in cancer research for understanding time-to-event outcomes. It involves specialized statistical methods to handle censored data and provide meaningful insights into survival experiences. The paper outlines the basic concepts and methods of survival analysis, emphasizing their importance in cancer studies.