Oncology Clinical Trials: Explanation and Interpretation of Kaplan-Meier (K-M) Survival Plot

 

The Kaplan-Meier (K-M) survival plot is a statistical tool used in clinical research to estimate and visualize time-to-event data, such as progression-free survival (PFS) or overall survival (OS). It helps compare the probability of survival (or remaining event-free) over time between different treatment groups.

Key Components of a K-M Survival Plot

  1. X-Axis (Time in Months/Years)

    • Represents time since treatment initiation or study enrollment.
    • Could be in months, years, or another unit depending on the study.

  2. Y-Axis (Survival Probability or PFS Probability)

    • Represents the proportion of patients still event-free (e.g., alive, progression-free) at a given time point.
    • Starts at 1 (100%) when all patients are event-free and decreases over time.

  3. Survival Curves (One for Each Group)

    • Each curve shows the estimated survival probability over time for a specific group (e.g., treatment vs. control).
    • A steeper drop indicates faster progression or death in that group.
    • If curves remain far apart, it suggests a significant difference between the groups.

  4. Censoring (Tick Marks on the Curve)

    • Small vertical tick marks represent censored patients, meaning:
      • The patient was still alive and event-free when the study ended.
      • The patient withdrew from the study before experiencing the event.
    • These patients contribute information until the time they were last observed.

  5. Median Survival Time (Horizontal Line at 0.5 Probability)

    • The time at which 50% of patients have experienced the event.
    • Example: Median PFS = 16.4 months means half of the patients had disease progression before 16.4 months, and the other half progressed later.

  6. Log-Rank Test and Hazard Ratio (HR)

    • A log-rank test is often used to compare survival curves statistically.
    • A hazard ratio (HR) quantifies the difference between groups:
      • HR < 1: Treatment reduces risk of event (beneficial).
      • HR > 1: Treatment increases risk of event (harmful).
      • HR = 1: No difference.

How to Interpret a Kaplan-Meier Survival Plot

  1. Separation Between Curves

    • Wider separation suggests better survival/PFS for one group.
    • If curves overlap, there may be no significant difference.

  2. Steepness of the Curve

    • A steep drop means many patients experienced the event quickly.
    • A gradual decline suggests longer event-free survival.

  3. Crossover Between Curves

    • If curves cross, it may indicate treatment effect changes over time.
    • Could suggest early benefit followed by later loss of effectiveness.

  4. Censoring Patterns

    • If many patients are censored early, interpretation becomes less reliable at later time points.

Example Interpretation

If a Kaplan-Meier plot shows:

  • The treatment group’s curve is consistently above the control group’s curve, it suggests longer survival/PFS with treatment.
  • A hazard ratio (HR) of 0.58 (95% CI: 0.49–0.70) means the treatment reduces the risk of progression/death by 42%.
  • A log-rank p-value < 0.05 suggests the difference is statistically significant.

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