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Hussain

hussainiabdullahi

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replied to ImranzamanML's post 17 days ago
Deep understanding of (C-index) evaluation measure for better model Lets start with three patients groups: Group A Group B Group C For each patient, we will predict risk score (higher score means higher risk of early event). Step 1: Understanding Concordance Index The Concordance Index (C-index) evaluate that how well the model ranks survival times. Understand with sample data: Group A has 3 patients with actual survival times and predicted risk scores: Patient Actual Survival Time Predicted Risk Score P1 5 months 0.8 P2 3 months 0.9 P3 10 months 0.2 Comparable pairs: (P1, P2): P2 has a shorter survival time and a higher risk score → Concordant ✅ (P1, P3): P3 has a longer survival time and a lower risk score → Concordant ✅ (P2, P3): P3 has a longer survival time and a lower risk score → Concordant ✅ Total pairs = 3 Total concordant pairs = 3 C-index for Group A = Concordant pairs/Total pairs= 3/3 = 1.0 Step 2: Calculate C-index for All Groups Repeat the process for all groups. For now we can assume: Group A: C-index = 1.0 Group B: C-index = 0.8 Group C: C-index = 0.6 Step 3: Stratified Concordance Index The Stratified Concordance Index combines the C-index scores of all groups and focusing on the following: Average performance across groups (mean of C-indices). Consistency across groups (low standard deviation of C-indices). Formula: Stratified C-index = Mean(C-index scores) - Standard Deviation(C-index scores) Calculate the mean: Mean=1.0 + 0.8 + 0.6/3 = 0.8 Calculate the standard deviation: Standard Deviation= sqrt((1.0-0.8)^2 + (0.8-0.8)^2 + (0.6-0.8)^/3) = 0.16 Stratified C-index: Stratified C-index = 0.8 - 0.16 = 0.64 Step 4: Interpret the Results A high Stratified C-index means: The model predicts well overall (high mean C-index).
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