Learning Objective
By the end of this module, students should be able to interpret Kaplan-Meier survival curves, understand key components, and apply statistical analysis to “time-to-event” outcomes.
Overview
The Kaplan-Meier curve is a statistical tool used in clinical research to estimate the probability of survival over time. It is especially useful for time-to-event analyses such as mortality, disease recurrence, or treatment failure.
Key Components of Kaplan-Meier Curve
| Component | Description |
|---|---|
| Y-axis | Probability of survival (0–1 scale) |
| X-axis | Time (days, months, years, depending on study) |
| Steps/Curve | Each drop corresponds to an event (e.g., death, relapse) |
| Censoring | Marks individuals lost to follow-up; shown as tick marks |
| Survival Probability Formula | S(t)=1−(event probability)S(t) = 1 – \text{(event probability)} |
| Statistical Tests | Log-rank test (comparison between groups), Cox regression (multivariable analysis) |
Key Points for USMLE Step 1
- Kaplan-Meier curves are ideal for visualizing survival differences between treatment groups.
- Censoring allows inclusion of patients lost to follow-up without biasing survival estimates.
- Log-rank test compares survival distributions across groups; Cox proportional hazards assesses the impact of multiple variables.
- Survival probabilities decrease in a stepwise fashion as events occur.
