Discrete variable survival curves are graphical tools used for analyzing and displaying survival data, particularly suitable for situations where survival time is a discrete variable. Survival data typically includes two key pieces of information: survival time and whether an event has occurred (such as death, disease recurrence, etc.). Discrete variable survival curves can help researchers intuitively compare the distribution of survival times across different groups, thereby analyzing which factors may affect survival time.

Input

The input file has the first column as ID, the second column as futime, the third column as fustat, and the fourth column as Stage, separated by \t.

Output

Survival Analysis Plot (Kaplan-Meier Curve) Description

Chart Purpose

This figure uses the Kaplan-Meier method to compare the differences in time-dependent survival probabilities among different patient groups categorized by a grouping variable.

How to Interpret This Chart

• Axes:

  • X-axis: Represents time since the start of the study (e.g., months or years).

  • Y-axis: Represents the cumulative survival probability at the corresponding time point.

• Curves:

  • Each colored curve represents a specific patient group.

  • A higher curve position indicates a better survival rate for that group.

  • Stepwise drops in the curve indicate that an endpoint event (e.g., death) occurred in that group.

• P-value:

  • The P-value (displayed in the top-right corner) is derived from the Log-rank test, which evaluates whether there is a statistically significant difference in survival curves among all groups.

  • A P-value < 0.05 is typically considered statistically significant, suggesting that survival differences between groups are unlikely due to random chance.