Kaplan–Meier Survival Curve
The Kaplan–Meier survival curve, commonly known as the Kaplan–Meier curve, is a statistical method widely used in medical and health research to visualize and estimate time-to-event data, particularly survival times.
Overview
- Purpose: The primary goal of the Kaplan–Meier curve is to depict how the probability of an event, such as survival, changes over time.
- Graph Components:
- X-axis: Typically represents time, which could be in days, months, or years.
- Y-axis: Represents the probability of survival or the percentage of subjects still alive or event-free.
Features
- Step Function: The curve is a step function. It remains flat when no events occur and takes a step down with each event.
- Censoring: A significant advantage of the Kaplan–Meier method is its capability to manage "censored" data. Data may be "censored" if a participant exits the study prematurely or if the event hasn't occurred by the study's conclusion. Censored observations are marked with small vertical tick marks on the curve.
- Comparative Analysis: Researchers can use Kaplan–Meier curves to compare survival times across two or more groups. This is especially useful for comparing the efficacy of different medical treatments.
Applications
The Kaplan–Meier survival curve is primarily applied in:
- Clinical trials
- Epidemiological studies
- Any research where time-to-event data is crucial.