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Table 2 True values and posterior estimates across 1000 simulated datasets of Area Under the Curve (AUC) and Integrated Discrimination Improvement (IDI) statistics obtained from latent class models assuming conditional independence (with S1=0.7, C1=0.9)

From: A Bayesian framework for estimating the incremental value of a diagnostic test in the absence of a gold standard

Accuracy of T2

Parameter value

AUC for T1 and T2

AUC for T1

AUC difference

IDI in events

IDI in non events

IDI*

1) Higher sensitivity

S2 = 80, C2 = 90

True

0.93

0.80

0.13

0.16

0.07

0.23

Estimated

0.93 (0.91, 0.95)

0.80 (0.79, 0.80)

0.13 (0.11, 0.15)

0.17 (0.12, 0.22)

0.07 (0.05, 0.10)

0.25 (0.18, 0.32)

2) Higher specificity

S2 = 70, C2 = 95

True

0.92

0.80

0.12

0.16

0.07

0.23

Estimated

0.94 (0.91. 0.96)

0.81 (0.80, 0.83)

0.12 (0.11, 0.14)

0.17 (0.12, 0.22)

0.07 (0.05, 0.09)

0.28 (0.22, 0.34)

3) Lower sensitivity

S2 = 60, C2 = 90

True

0.88

0.80

0.09

0.09

0.04

0.12

Estimated

0.89 (0.87, 0.91)

0.80 (0.79, 0.80)

0.09 (0.07, 0.11)

0.10 (0.06, 0.14)

0.04 (0.03, 0.06)

0.14 (0.09, 0.20)

4) Lower specificity

S2 = 70, C2 = 80

True

0.88

0.80

0.09

0.07

0.03

0.10

Estimated

0.89 (0.87, 0.91)

0.80 (0.79, 0.80)

0.09 (0.07, 0.11)

0.08 (0.05, 0.12)

0.03 (0.02, 0.05)

0.11 (0.07, 0.17)

5) Both better

S2 = 80, C2 = 95

True

0.94

0.80

0.14

0.21

0.09

0.30

Estimated

0.94 (0.92, 0.96)

0.80 (0.80, 0.81)

0.14 (0.12, 0.17)

0.21 (0.16, 0.26)

0.09 (0.07, 0.11)

0.30 (0.23, 0.36)

6) Both worse

S2 = 60, C2 = 80

True

0.87

0.80

0.07

0.05

0.02

0.07

Estimated

0.87 (0.85, 0.89)

0.80 (0.79, 0.80)

0.07 (0.05, 0.09)

0.05 (0.03, 0.08)

0.02 (0.01, 0.04)

0.07 (0.04, 0.12)

7) No better

S2 = 70, C2 = 90

True

0.90

0.80

0.10

0.12

0.05

0.17

Estimated

0.91 (0.89, 0.94)

0.80 (0.79, 0.80)

0.11 (0.09, 0.14)

0.13 (0.09, 0.19)

0.06 (0.04, 0.08)

0.19 (0.13, 0.27)

8) No value

S2 = 70, C2 = 30

True

0.80

0.80

-0.00

-0.00

0.00

-0.00

Estimated

0.81 (0.80, 0.82)

0.80 (0.79, 0.80)

0.01 (0.006, 0.02)

0.001 (0, 0.004)

<0.001 (0, 0.002)

0.002 (0.001, 0.006)

  1. T1 = test 1; T2 = test 2; S 2  = sensitivity of T2; C 2  = specificity of T2.
  2. *IDI is sum of IDIevents and IDInon-events.