gh-k-dense-ai-claude-scientific-skills-scientific-skills/skills/scikit-survival/references/evaluation-metrics.md

Evaluation Metrics for Survival Models

Overview

Evaluating survival models requires specialized metrics that account for censored data. scikit-survival provides three main categories of metrics:

1. Concordance Index (C-index)
2. Time-dependent ROC and AUC
3. Brier Score

Concordance Index (C-index)

What It Measures

The concordance index measures the rank correlation between predicted risk scores and observed event times. It represents the probability that, for a random pair of subjects, the model correctly orders their survival times.

Range: 0 to 1

• 0.5 = random predictions
• 1.0 = perfect concordance
• Typical good performance: 0.7-0.8

Two Implementations

Harrell's C-index (concordance_index_censored)

The traditional estimator, simpler but has limitations.

When to Use:

• Low censoring rates (< 40%)
• Quick evaluation during development
• Comparing models on same dataset

Limitations:

• Becomes increasingly biased with high censoring rates
• Overestimates performance starting at approximately 49% censoring

from sksurv.metrics import concordance_index_censored

# Compute Harrell's C-index
result = concordance_index_censored(y_test['event'], y_test['time'], risk_scores)
c_index = result[0]
print(f"Harrell's C-index: {c_index:.3f}")

Uno's C-index (concordance_index_ipcw)

Inverse probability of censoring weighted (IPCW) estimator that corrects for censoring bias.

When to Use:

• Moderate to high censoring rates (> 40%)
• Need unbiased estimates
• Comparing models across different datasets
• Publishing results (more robust)

Advantages:

• Remains stable even with high censoring
• More reliable estimates
• Less biased

from sksurv.metrics import concordance_index_ipcw

# Compute Uno's C-index
# Requires training data for IPCW calculation
c_index, concordant, discordant, tied_risk = concordance_index_ipcw(
    y_train, y_test, risk_scores
)
print(f"Uno's C-index: {c_index:.3f}")

Choosing Between Harrell's and Uno's

Use Uno's C-index when:

• Censoring rate > 40%
• Need most accurate estimates
• Comparing models from different studies
• Publishing research

Use Harrell's C-index when:

• Low censoring rates
• Quick model comparisons during development
• Computational efficiency is critical

Example Comparison

from sksurv.metrics import concordance_index_censored, concordance_index_ipcw

# Harrell's C-index
harrell = concordance_index_censored(y_test['event'], y_test['time'], risk_scores)[0]

# Uno's C-index
uno = concordance_index_ipcw(y_train, y_test, risk_scores)[0]

print(f"Harrell's C-index: {harrell:.3f}")
print(f"Uno's C-index: {uno:.3f}")

Time-Dependent ROC and AUC

What It Measures

Time-dependent AUC evaluates model discrimination at specific time points. It distinguishes subjects who experience events by time t from those who don't.

Question answered: "How well does the model predict who will have an event by time t?"

When to Use

• Predicting event occurrence within specific time windows
• Clinical decision-making at specific timepoints (e.g., 5-year survival)
• Want to evaluate performance across different time horizons
• Need both discrimination and timing information

Key Function: cumulative_dynamic_auc

from sksurv.metrics import cumulative_dynamic_auc

# Define evaluation times
times = [365, 730, 1095, 1460, 1825]  # 1, 2, 3, 4, 5 years

# Compute time-dependent AUC
auc, mean_auc = cumulative_dynamic_auc(
    y_train, y_test, risk_scores, times
)

# Plot AUC over time
import matplotlib.pyplot as plt
plt.plot(times, auc, marker='o')
plt.xlabel('Time (days)')
plt.ylabel('Time-dependent AUC')
plt.title('Model Discrimination Over Time')
plt.show()

print(f"Mean AUC: {mean_auc:.3f}")

Interpretation

• AUC at time t: Probability model correctly ranks a subject who had event by time t above one who didn't
• Varying AUC over time: Indicates model performance changes with time horizon
• Mean AUC: Overall summary of discrimination across all time points

Example: Comparing Models

# Compare two models
auc1, mean_auc1 = cumulative_dynamic_auc(y_train, y_test, risk_scores1, times)
auc2, mean_auc2 = cumulative_dynamic_auc(y_train, y_test, risk_scores2, times)

plt.plot(times, auc1, marker='o', label='Model 1')
plt.plot(times, auc2, marker='s', label='Model 2')
plt.xlabel('Time (days)')
plt.ylabel('Time-dependent AUC')
plt.legend()
plt.show()

Brier Score

What It Measures

Brier score extends mean squared error to survival data with censoring. It measures both discrimination (ranking) and calibration (accuracy of predicted probabilities).

Formula: (1/n) Σ (S(t|x_i) - I(T_i > t))²

where S(t|x_i) is predicted survival probability at time t for subject i.

Range: 0 to 1

• 0 = perfect predictions
• Lower is better
• Typical good performance: < 0.2

When to Use

• Need calibration assessment (not just ranking)
• Want to evaluate predicted probabilities, not just risk scores
• Comparing models that output survival functions
• Clinical applications requiring probability estimates

Key Functions

brier_score: Single Time Point

from sksurv.metrics import brier_score

# Compute Brier score at specific time
time_point = 1825  # 5 years
surv_probs = model.predict_survival_function(X_test)
# Extract survival probability at time_point for each subject
surv_at_t = [fn(time_point) for fn in surv_probs]

bs = brier_score(y_train, y_test, surv_at_t, time_point)[1]
print(f"Brier score at {time_point} days: {bs:.3f}")

integrated_brier_score: Summary Across Time

from sksurv.metrics import integrated_brier_score

# Compute integrated Brier score
times = [365, 730, 1095, 1460, 1825]
surv_probs = model.predict_survival_function(X_test)

ibs = integrated_brier_score(y_train, y_test, surv_probs, times)
print(f"Integrated Brier Score: {ibs:.3f}")

Interpretation

• Brier score at time t: Expected squared difference between predicted and actual survival at time t
• Integrated Brier Score: Weighted average of Brier scores across time
• Lower values = better predictions

Comparison with Null Model

Always compare against a baseline (e.g., Kaplan-Meier):

from sksurv.nonparametric import kaplan_meier_estimator

# Compute Kaplan-Meier baseline
time_km, surv_km = kaplan_meier_estimator(y_train['event'], y_train['time'])

# Predict with KM for each test subject
surv_km_test = [surv_km[time_km <= time_point][-1] if any(time_km <= time_point) else 1.0
                for _ in range(len(X_test))]

bs_km = brier_score(y_train, y_test, surv_km_test, time_point)[1]
bs_model = brier_score(y_train, y_test, surv_at_t, time_point)[1]

print(f"Kaplan-Meier Brier Score: {bs_km:.3f}")
print(f"Model Brier Score: {bs_model:.3f}")
print(f"Improvement: {(bs_km - bs_model) / bs_km * 100:.1f}%")

Using Metrics with Cross-Validation

Concordance Index Scorer

from sklearn.model_selection import cross_val_score
from sksurv.metrics import as_concordance_index_ipcw_scorer

# Create scorer
scorer = as_concordance_index_ipcw_scorer()

# Perform cross-validation
scores = cross_val_score(model, X, y, cv=5, scoring=scorer)
print(f"Mean C-index: {scores.mean():.3f} (±{scores.std():.3f})")

Integrated Brier Score Scorer

from sksurv.metrics import as_integrated_brier_score_scorer

# Define time points for evaluation
times = np.percentile(y['time'][y['event']], [25, 50, 75])

# Create scorer
scorer = as_integrated_brier_score_scorer(times)

# Perform cross-validation
scores = cross_val_score(model, X, y, cv=5, scoring=scorer)
print(f"Mean IBS: {scores.mean():.3f} (±{scores.std():.3f})")

Model Selection with GridSearchCV

from sklearn.model_selection import GridSearchCV
from sksurv.ensemble import RandomSurvivalForest
from sksurv.metrics import as_concordance_index_ipcw_scorer

# Define parameter grid
param_grid = {
    'n_estimators': [100, 200, 300],
    'min_samples_split': [10, 20, 30],
    'max_depth': [None, 10, 20]
}

# Create scorer
scorer = as_concordance_index_ipcw_scorer()

# Perform grid search
cv = GridSearchCV(
    RandomSurvivalForest(random_state=42),
    param_grid,
    scoring=scorer,
    cv=5,
    n_jobs=-1
)
cv.fit(X, y)

print(f"Best parameters: {cv.best_params_}")
print(f"Best C-index: {cv.best_score_:.3f}")

Comprehensive Model Evaluation

Recommended Evaluation Pipeline

from sksurv.metrics import (
    concordance_index_censored,
    concordance_index_ipcw,
    cumulative_dynamic_auc,
    integrated_brier_score
)

def evaluate_survival_model(model, X_train, X_test, y_train, y_test):
    """Comprehensive evaluation of survival model"""

    # Get predictions
    risk_scores = model.predict(X_test)
    surv_funcs = model.predict_survival_function(X_test)

    # 1. Concordance Index (both versions)
    c_harrell = concordance_index_censored(y_test['event'], y_test['time'], risk_scores)[0]
    c_uno = concordance_index_ipcw(y_train, y_test, risk_scores)[0]

    # 2. Time-dependent AUC
    times = np.percentile(y_test['time'][y_test['event']], [25, 50, 75])
    auc, mean_auc = cumulative_dynamic_auc(y_train, y_test, risk_scores, times)

    # 3. Integrated Brier Score
    ibs = integrated_brier_score(y_train, y_test, surv_funcs, times)

    # Print results
    print("=" * 50)
    print("Model Evaluation Results")
    print("=" * 50)
    print(f"Harrell's C-index:  {c_harrell:.3f}")
    print(f"Uno's C-index:      {c_uno:.3f}")
    print(f"Mean AUC:           {mean_auc:.3f}")
    print(f"Integrated Brier:   {ibs:.3f}")
    print("=" * 50)

    return {
        'c_harrell': c_harrell,
        'c_uno': c_uno,
        'mean_auc': mean_auc,
        'ibs': ibs,
        'time_auc': dict(zip(times, auc))
    }

# Use the evaluation function
results = evaluate_survival_model(model, X_train, X_test, y_train, y_test)

Choosing the Right Metric

Decision Guide

Use C-index (Uno's) when:

• Primary goal is ranking/discrimination
• Don't need calibrated probabilities
• Standard survival analysis setting
• Most common choice

Use Time-dependent AUC when:

• Need discrimination at specific time points
• Clinical decisions at specific horizons
• Want to understand how performance varies over time

Use Brier Score when:

• Need calibrated probability estimates
• Both discrimination AND calibration important
• Clinical decision-making requiring probabilities
• Want comprehensive assessment

Best Practice: Report multiple metrics for comprehensive evaluation. At minimum, report:

• Uno's C-index (discrimination)
• Integrated Brier Score (discrimination + calibration)
• Time-dependent AUC at clinically relevant time points