Comparison of Two Bayesian Methods in Evaluation of the Absence of the Gold Standard Diagnostic Tests
Table 5
The posterior estimation of four models using the TB data under conditional independence situation.
Situation
Method
Model
Knot
Mean
SD
Median
95% Bayesian CI
(P2.5-P97.5)
No prior constraints
Bayesian probabilistic constraint model
NP
0.660
0.243
0.762
0.503-0.841
0.418
0.251
0.426
0.203-0.584
0.511
0.251
0.568
0.302-0.688
0.569
0.249
0.631
0.377-0.759
0.512
0.194
0.515
0.361-0.667
Conditional Covariance Bayesian model
NC
0.618
0.250
0.631
0.071-0.974
0.377
0.250
0.295
0.025-0.929
0.468
0.255
0.435
0.036-0.947
0.526
0.253
0.480
0.055-0.962
0.498
0.194
0.497
0.148-0.850
Prior constraints
Bayesian probabilistic constraint model
PP
0.738
0.036
0.739
0.714-0.762
0.454
0.051
0.453
0.419-0.487
0.585
0.031
0.585
0.563-0.606
0.525
0.028
0.525
0.506-0.544
0.534
0.042
0.534
0.506-0.562
Conditional Covariance Bayesian model
PC
0.898
0.039
0.910
0.814-0.963
0.765
0.125
0.775
0.515-0.968
0.594
0.048
0.586
0.523-0.712
0.679
0.048
0.676
0.592-0.780
0.636
0.086
0.650
0.435-0.773
: sensitivity of T-SPOT; : specificity of T-SPOT; : sensitivity of KD38; : specificity of KD38;: prevalence within the patients in the study; SD: standard deviation; CI: confidence interval.