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Table 5 Performance of penalized methods in predicting cardiac events

From: Performance of Firth-and logF-type penalized methods in risk prediction for small or sparse binary data

Models for predicting the risk of MI (EPV ≈ 7)

Methods

Calibration Slope

AUC

Brier Score

APP

MLE

0.696(0.258)

0.768(0.051)

0.047

0.051

Firth

0.706(0.260)

0.766(0.052)

0.049

0.057

logF(1,1)

0.713 (0.265)

0.769(0.051)

0.048

0.052

logF(2,2)

0.723 (0.271)

0.769(0.051)

0.048

0.052

RIDGE

0.772(0.309)

0.762(0.053)

0.047

0.050

Models for predicting the risk of CABG (EPV ≈ 10)

MLE

0.912(0.219)

0.814(0.046)

0.057

0.056

Firth

0.909 (0.217)

0.814 (0.046)

0.056

0.059

logF(1,1)

0.921(0.221)

0.814(0.046)

0.056

0.055

logF(2,2)

0.926(0.223)

0.813(0.046)

0.057

0.055

RIDGE

0.886(0.217)

0.814(0.046)

0.057

0.055

Models for predicting the risk of PTCA (EPV ≈ 5)

MLE

0.718 (0.291)

0.730(0.108)

0.034

0.061

Firth

0.721(0.279)

0.729(0.108)

0.035

0.066

logF(1,1)

0.721(0.298)

0.728(0.107)

0.034

0.061

logF(2,2)

0.720(0.305)

0.728(0.107)

0.034

0.061

RIDGE

0.774(0.544)

0.727(0.107)

0.033

0.061

Models for predicting the risk of cardiac death (EPV ≈ 6)

MLE

0.661(0.529)

0.688(0.121)

0.024

0.062

Firth

0.680(0.545)

0.688 (0.121)

0.024

0.067

logF(1,1)

0.645(0.535)

0.687(0.120)

0.024

0.062

logF(2,2)

0.623 (0.538)

0.687 (0.120)

0.024

0.061

RIDGE

0.665 (0.608)

0.684 (0.121)

0.023

0.062

Models for predicting the risk of any cardiac event (EPV ≈ 15)

MLE

0.942(0.206)

0.771(0.044)

0.059

0.164

Firth

0.946(0.207)

0.767 (0.044)

0.059

0.167

logF(1,1)

0.945(0.206)

0.770(0.044)

0.058

0.164

logF(2,2)

0.946(0.207)

0.770 (0.044)

0.058

0.164

RIDGE

1.004(0.222)

0.769(0.044)

0.056

0.165

  1. Event Per Variable (EPV) was calculated based on the number of event in training data. Estimates of the performance measures with SE in the parenthesis