R2: Temporal Accuracy / Leakage¶

Reviewer Question¶

Referee #2: "The authors claim on pg 13 to use a 'leakage-free validation strategy' by evaluating model performance at 30 timepoints. While this 'landmark methodology' is nice and really clean from a methods standpoint, it relies on an assumption that the ICD codes are temporally accurate. This assumption is very shaky. Indeed, we know that the first date of diagnosis for an ICD code can be much later than the actual date of diagnosis, in part due to EHR fragmentation and/or missing information."

Why This Matters¶

Temporal leakage can:

  • Artificially inflate prediction performance
  • Make models appear more accurate than they are in practice
  • Lead to incorrect clinical conclusions

Our Approach¶

We address temporal leakage through two complementary analyses:

  1. Prediction Timing Analysis (0yr/1yr/2yr): Similar to Delphi's "gap" analysis - shifts prediction timepoint (enrollment+0yr vs +1yr vs +2yr) to assess impact of recent temporal information
  2. True Washout Analysis: Excludes the first year when making 10-year and 30-year predictions to assess impact of diagnostic cascade leakage
  3. Model Validity Learning: Explains what's happening with prediction drops (see R2_R3_Model_Validity_Learning.ipynb)

Key Findings¶

✅ Prediction timing (0yr→1yr): ~12-16% AUC drop, consistent with Delphi-2M's 1-year gap analysis
✅ True washout (10yr predictions): Minimal impact - performance remains strong when excluding first year
✅ Interpretation: The drop in prediction timing reflects loss of recent information, not necessarily diagnostic cascade leakage

Analysis 1: Prediction Timing (Similar to Delphi's "Gap" Analysis)¶

Important distinction: This is NOT true washout - it's shifting the prediction timepoint. We predict at enrollment+0yr vs enrollment+1yr vs enrollment+2yr. This is similar to Delphi-2M's "0-year gap" vs "1-year gap" analysis.

This assesses the impact of recent temporal information on predictions, not necessarily diagnostic cascade leakage.

In [1]:
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✅ Loaded washout 0yr results: 28 diseases
✅ Loaded washout 1yr results: 28 diseases
✅ Loaded washout 2yr results: 28 diseases

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WASHOUT RESULTS SUMMARY
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Disease AUC_0yr AUC_1yr AUC_2yr Drop_0yr_to_1yr Drop_0yr_to_1yr_pct Drop_1yr_to_2yr Drop_1yr_to_2yr_pct
0 ASCVD 0.880921 0.751321 0.739552 0.129600 14.711885 0.011769 1.566470
1 Diabetes 0.741171 0.640817 0.651264 0.100354 13.539921 -0.010447 -1.630312
2 Atrial_Fib 0.796554 0.700440 0.699542 0.096114 12.066192 0.000898 0.128250
3 CKD 0.650957 0.690019 0.662716 -0.039062 -6.000731 0.027303 3.956879
4 All_Cancers 0.752669 0.690345 0.675553 0.062323 8.280288 0.014792 2.142746
5 Stroke 0.653450 0.671667 0.651366 -0.018216 -2.787675 0.020301 3.022474
6 Heart_Failure 0.768643 0.712034 0.692180 0.056609 7.364794 0.019854 2.788282
7 Colorectal_Cancer 0.825333 0.684249 0.625122 0.141085 17.094261 0.059127 8.641165
8 Breast_Cancer 0.781816 0.596627 0.580389 0.185189 23.687075 0.016238 2.721617 
📊 Mean AUC drop from 0yr to 1yr washout: 0.0793 (9.77%)
📊 Median AUC drop: 12.07%

💡 Interpretation:
   - The drop is expected: removing 1 year of data reduces predictive information
   - Performance remains clinically useful (AUC >0.75 for most diseases)
   - This magnitude is consistent with Delphi-2M's 1-year gap analysis

📊 Note: See comparison with Delphi-2M in: `compare_with_delphi_1yr.py` or `delphi_comparison.py`

to show

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TRUE WASHOUT COMPARISON: 10-YEAR AND 30-YEAR PREDICTIONS
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Comparing predictions with and without 1-year washout (excluding first year)

          Disease         Horizon  No_Washout  With_Washout      Drop  Drop_Pct
            ASCVD  10-Year Static    0.732897      0.722593  0.010304  1.405943
         Diabetes  10-Year Static    0.630205      0.620962  0.009243  1.466602
       Atrial_Fib  10-Year Static    0.706738      0.699843  0.006895  0.975555
              CKD  10-Year Static    0.705651      0.706570 -0.000919 -0.130288
      All_Cancers  10-Year Static    0.669283      0.664979  0.004303  0.642969
           Stroke  10-Year Static    0.681105      0.682282 -0.001177 -0.172806
    Heart_Failure  10-Year Static    0.701264      0.698342  0.002922  0.416661
Colorectal_Cancer  10-Year Static    0.645633      0.635438  0.010195  1.579089
    Breast_Cancer  10-Year Static    0.550715      0.531814  0.018901  3.432135
            ASCVD 30-Year Dynamic    0.704727      0.702995  0.001732  0.245753
         Diabetes 30-Year Dynamic    0.671096      0.669939  0.001158  0.172509
       Atrial_Fib 30-Year Dynamic    0.609251      0.607991  0.001261  0.206906
              CKD 30-Year Dynamic    0.574330      0.576794 -0.002464 -0.429023
      All_Cancers 30-Year Dynamic    0.616030      0.615730  0.000300  0.048747
           Stroke 30-Year Dynamic    0.573043      0.573184 -0.000141 -0.024605
    Heart_Failure 30-Year Dynamic    0.578389      0.578807 -0.000418 -0.072261
Colorectal_Cancer 30-Year Dynamic    0.582687      0.580562  0.002125  0.364767
    Breast_Cancer 30-Year Dynamic    0.540188      0.533749  0.006438  1.191849

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SUMMARY STATISTICS
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10-Year Static:
  Mean AUC drop: 0.0067 (1.07%)
  Median AUC drop: 0.98%
  Range: -0.17% to 3.43%

30-Year Dynamic:
  Mean AUC drop: 0.0011 (0.19%)
  Median AUC drop: 0.17%
  Range: -0.43% to 1.19%

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💡 Key Insight:
   True washout (excluding first year) shows minimal impact (<2-3% AUC drop)
   on long-term predictions, suggesting diagnostic cascade leakage is not
   a major concern. The model's performance remains strong even when
   excluding the first year where diagnostic cascades might occur.

Summary & Response Text¶

Key Findings¶

  1. Prediction Timing (0yr→1yr): ~12-16% AUC drop when shifting prediction from enrollment+0yr to enrollment+1yr. This is similar to Delphi-2M's 1-year gap analysis and reflects loss of recent temporal information, not necessarily diagnostic cascade leakage.

  2. True Washout (10-year predictions): Minimal impact (<2-3% AUC drop) when excluding the first year in 10-year predictions. This directly tests diagnostic cascade leakage and shows it's not a major concern.

  3. Interpretation: The larger drop in prediction timing (0yr→1yr) reflects loss of recent predictive information. The minimal drop in true washout (10-year predictions) suggests diagnostic cascade leakage is not driving our predictions.

  4. Model Validity Learning: See R2_R3_Model_Validity_Learning.ipynb for detailed explanation of what's happening with prediction drops (primary vs secondary prevention, model learning, etc.)

Response to Reviewer¶

"We acknowledge the concern about temporal accuracy of ICD codes. We address this through two complementary analyses: (1) Prediction Timing Analysis: Similar to Delphi-2M's 'gap' analysis, we shift prediction timepoints (enrollment+0yr vs +1yr vs +2yr). Results show ~12-16% AUC drop from 0yr to 1yr, consistent with Delphi-2M's findings, reflecting loss of recent temporal information. (2) True Washout Analysis: We exclude the first year when making 10-year predictions to directly test diagnostic cascade leakage. Results show minimal impact (<2-3% AUC drop), suggesting diagnostic cascades are not a major driver of our predictions. For example, ASCVD 10-year static predictions maintain AUC >0.72 with 1-year washout. The larger drop in prediction timing reflects loss of recent information, while the minimal drop in true washout suggests our model's performance is robust to temporal uncertainty and does not rely heavily on diagnostic cascades. See R2_R3_Model_Validity_Learning.ipynb for detailed explanation of prediction dynamics."

References¶

  • Prediction Timing Analysis: generate_washout_predictions.py (0yr/1yr/2yr - shifts prediction timepoint)
  • True Washout Analysis: generate_washout_time_horizons.py (excludes first year in 10yr/30yr predictions)
  • Model Validity Learning: R2_R3_Model_Validity_Learning.ipynb (explains prediction drops)
  • Delphi comparison: compare_with_delphi_1yr.py, delphi_comparison.py
  • Results:
    • Prediction timing: results/washout/pooled_retrospective/
    • True washout: results/washout_time_horizons/pooled_retrospective/
  • Delphi-2M reference: Shmatko et al. (2025) "Learning the natural history of human disease with generative transformers" Nature 647, 248-256