Purpose:
The survival ROC curve is a tool for evaluating the performance of survival prediction models, particularly for time-to-event data. Unlike traditional ROC curves, survival ROC curves incorporate time-dependent factors, allowing assessment of predictive performance at different time points.

Input File Format:
The input file contains four tab-delimited columns (\t):

1. ID: Sample/patient identifier

2. futime: Follow-up time (time-to-event or censoring)

3. fustat: Event status (typically 0=censored, 1=event occurred)

4. score: Predicted risk score/probability from the model

Ootput File Format:

Chart Description

Chart Type: Time-dependent ROC Curve
X-axis: False Positive Rate (1 - Specificity)
Y-axis: True Positive Rate (Sensitivity)
Red Curve: Represents the predictive performance of the selected variable (e.g., score) for survival outcomes at a specified time point (e.g., 1 year).
AUC Value: The chart displays the AUC (Area Under Curve) value.

• An AUC closer to 1 indicates stronger predictive ability at that time point.

• An AUC of 0.5 suggests predictive performance equivalent to random guessing.

Interpretation Guidance:

• This plot evaluates the predictive efficacy of continuous variables (e.g., risk scores, gene expression) for survival outcomes at specific time points (e.g., 1 year).

• A curve closer to the top-left corner and a higher AUC value indicate better predictive performance.

• Applicable to survival analysis and prognostic model validation scenarios.