Kaplan-Meier survival analysis is a statistical technique used to estimate the probability of survival over time in medical research, particularly in studies where the outcome of interest is time until an event occurs, such as death, disease recurrence, or treatment failure. This method accounts for censored data, where some individuals have not experienced the event of interest by the end of the study period or are lost to follow-up. Kaplan-Meier curves display the cumulative probability of survival at various time points, providing valuable insights into the survival experience of study participants.
To perform Kaplan-Meier survival analysis, researchers typically input data on the time to event (survival time) and the status of each participant (event occurrence or censoring). Using this information, the Kaplan-Meier estimator calculates the probability of survival at each time point, considering both individuals who have experienced the event and those who remain at risk. The resulting Kaplan-Meier curve visually depicts the survival function over time, illustrating how survival probabilities change as time progresses.
One of the primary benefits of Kaplan-Meier survival analysis is its flexibility and ability to handle various types of censoring, making it suitable for analyzing survival data in a wide range of research settings. Additionally, Kaplan-Meier curves provide valuable information on the median survival time, as well as survival rates at specific time points of interest. This method is widely used in clinical trials, epidemiological studies, and observational research to assess the efficacy of treatments, identify prognostic factors, and compare survival outcomes between different groups. Overall, Kaplan-Meier survival analysis is a powerful tool for analyzing time-to-event data and understanding the dynamics of survival in medical research.
