How to Understand Survival Probability Using the Kaplan–Meier Method — and Why Censoring Matters
Survival analysis is a core component of clinical research, especially in oncology and rare disease trials where time-to-event endpoints such as PFS, OS, or time to loss of ambulation play a central role. Among all methods, the Kaplan–Meier (KM) estimator remains the most widely used tool to calculate and visualize survival probability. However, its interpretation is often misunderstood—particularly when censoring is heavy or uneven across groups. This article provides a clear, practical guide covering: How survival probability is calculated using the KM method How censored observations impact the KM curve and interpretation How to simulate survival data in R and SAS to visualize the effect of censoring 1. How the Kaplan–Meier Method Calculates Survival Probability The Kaplan–Meier (KM) estimator is a nonparametric method to estimate the survival function, S ( t ) S(t) from time-to-event data that may include censoring. KM produces a step function that updates on...