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# Table 2 Comparison of different statistical tests in assessing whether a new biomarker has added value

Null hypothesis

N 0=60

N 0=120

N 0=120

N 0=240

N 0=480

(Type I error)

N 1=30

N 1=60

N 1=120

N 1=120

N 1=480

LR test

0.1033

0.0668

0.0585

0.0600

0.0546

Wald Test

0.0781

0.0608

0.0546

0.0571

0.0538

F test of ideal AUC

0.0495

0.0482

0.0481

0.0522

0.0515

Alt. Hypothesis

N 0=60

N 0=120

N 0=120

N 0=240

N 0=480

(Power)

N 1=30

N 1=60

N 1=120

N 1=120

N 1=480

LR test

0.5956

0.8569

0.9539

0.9896

1.0000

Wald Test

0.5394

0.8453

0.9506

0.9889

1.0000

F test of ideal AUC

0.5196

0.8514

0.9538

0.9915

1.0000

1. Fraction of significant findings in 20,000 Monte Carlo trials at statistical significance cut-off of 0.05. On the top are the results for the null-hypothesis experiment where the fraction of significant findings is the observed type I error rate. At the bottom are the results for the alternative-hypothesis experiment where the fraction of significant findings is the statistical power.