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.

Comments

Post a Comment

Popular posts from this blog

Analysis of Repeated Measures Data using SAS (1)

Image
 1. Basic Concepts of Repeated Measures: In this basic setup of a completely randomized design with repeated measures, there are two factors, treatments and time. Treatment is called the between-subjects factor because levels of treatment can change only between subjects; all measurements on the same subject will represent the same treatment. Time is called a within-subjects factor because different measurements on the same subject are taken at different times.  In repeated measures experiments, interest centers on (1) how treatment means differ, (2) how treatment means change over time, and (3) how differences between treatment means change over time. 2. Four-step procedure for mixed model analysis: Step 1: Model the mean structure, usually by specification of the fixed effects. Step 2: Specify the covariance structure, between subjects as well as within subjects. Step 3: Fit the mean model accounting for the covariance structure. Step 4: Make statistical inference bas...
Read more

Medical information for Melanoma, Merkel cell carcinoma and tumor mutation burden

  Background: Melanoma Also called: malignant melanoma The most serious type of skin cancer. Melanoma occurs when the pigment-producing cells that give color to the skin become cancerous. Symptoms might include a new, unusual growth or a change in an existing mole. Melanomas can occur anywhere on the body. Treatment may involve surgery, radiation, medications, or in some cases chemotherapy. Merkel cell carcinoma  (MCC) it is a very rare ,  highly aggressive, neuroendocrine carcinoma of the skin ;  it has a worse survival probability compared with more common cutaneous malignancies, including metastatic melanoma, and has been termed the “most lethal” skin cancer  as   p atients with metastatic MCC have a historical 5-year overall survival (OS) rate of 18% What Is Tumor Mutational Burden? TMB is defined as the frequency of certain mutations within a tumor’s genes.  To be counted towards TMB, mutations have to alter the protein that is made from a gene. ...
Read more

Four essential statistical functions for simulation in SAS

  PDF function : This function is the probability density function. It returns the probability density at a given point for a variety of distributions. (For discrete distribution, the PDF function evaluates the probability mass function.) CDF function : This function is the cumulative distribution function. The CDF returns the probability that an observation from the specified distribution is less than or equal to a particular value. For continuous distributions, this is the area under the PDF up to a certain point. QUANTILE function : This function is closely related to the CDF function, but solves an inverse problem. Given a probability, P, it returns the smallest value,  q , for which CDF( q ) is greater than or equal to P. RAND function : This function generates a random sample from a distribution. In SAS/IML software, use the  RANDGEN subroutine , which fills up an entire matrix at once.
Read more