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	Se 1	0.660	0.243	0.762	0.503-0.841
			Sp 1	0.418	0.251	0.426	0.203-0.584
			Se 2	0.511	0.251	0.568	0.302-0.688
			Sp 2	0.569	0.249	0.631	0.377-0.759
			p	0.512	0.194	0.515	0.361-0.667
	Conditional Covariance Bayesian model	NC	Se 1	0.618	0.250	0.631	0.071-0.974
			Sp 1	0.377	0.250	0.295	0.025-0.929
			Se 2	0.468	0.255	0.435	0.036-0.947
			Sp 2	0.526	0.253	0.480	0.055-0.962
			p	0.498	0.194	0.497	0.148-0.850
Prior constraints	Bayesian probabilistic constraint model	PP	Se 1	0.738	0.036	0.739	0.714-0.762
			Sp 1	0.454	0.051	0.453	0.419-0.487
			Se 2	0.585	0.031	0.585	0.563-0.606
			Sp 2	0.525	0.028	0.525	0.506-0.544
			p	0.534	0.042	0.534	0.506-0.562
	Conditional Covariance Bayesian model	PC	Se 1	0.898	0.039	0.910	0.814-0.963
			Sp 1	0.765	0.125	0.775	0.515-0.968
			Se 2	0.594	0.048	0.586	0.523-0.712
			Sp 2	0.679	0.048	0.676	0.592-0.780
			p	0.636	0.086	0.650	0.435-0.773

Se ₁: sensitivity of T-SPOT; Sp₁: specificity of T-SPOT; Se₂: sensitivity of KD38; Sp₂: specificity of KD38;  p: prevalence within the patients in the study; SD: standard deviation; CI: confidence interval.