Decision curve analysis

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Conclusion 1Prediction modelsare wonderful!How do you knowthat they do moregood than harm?• Traditional biostatistical techniquesfor evaluating models, markers andtests do not incorporate clinicalconsequences• Accordingly, they cannot informclinical practice
CanceraApplymodelBiopsyNo biopsyNo cancerCancerCancerNo cancer Cancerp 1p 2p 31- (p 1 + p 2 + p 3 )bcdCancerCancer(p 1 + p 3 )aBiopsyNo cancerCancer1 - (p 1 + p 3 )bNo biopsyCancerNo cancer Cancer(p 1 + p 3 )1 - (p 1 + p 3 )cd
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 16 and 17: AUC / C indexProbability correct id
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 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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