Decision curve analysis

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AUC / C indexProbability correct identification ofdiscordant pair
AUC / C indexProbability correct identification ofdiscordant pairClassifying a patient without an eventas high probability =classifying a patient with an event aslow probability– Pr: 50%, D=0; Pr: 10%, D=1– Pr: 50%, D=1; Pr: 90%, D=0
Page 1 and 2: Decision curve analysisAndrew Vicke
Page 3 and 4: What am I doing here?Prediction mod
Page 5: How do you know thatprediction mode
Page 8 and 9: ResultsMarker Sensitivity Specifici
Page 10 and 11: ResultsMarker Sensitivity Specifici
Page 12 and 13: Principle• Sensitivity / specific
Page 14 and 15: Youden IndexSensitivity + Specifici
Page 18 and 19: Brier Score(D - P) 2
Page 20 and 21: Net Reclassification Index
Page 22 and 23: When is it ever the case inmedicine
Page 24 and 25: Conclusion 1Prediction modelsare wo
Page 26 and 27: CanceraApplymodelBiopsyNo biopsyNo
Page 28 and 29: Threshold probabilityProbability of
Page 30 and 31: Intuitively• The threshold probab
Page 32 and 33: Illustrative example• Men with ra
Page 34 and 35: Free PSA
Page 36: Net benefit has simpleclinical inte
Page 39 and 40: Test 2Test 1
Page 41 and 42: Incorporating the harm of atestTrue
Page 44 and 45: Traditionalstatistical analysisTrad
Page 46 and 47: Software for Decision CurveAnalysis
Page 48 and 49: Extensions to decisioncurve analysi

Decision curve analysis

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