Table 7.

The posterior estimation of four models using the TB data under conditional dependence situation.

Situation	Method	Model	Knot	Mean	SD	Median	95% Bayesian CI
							(P25-P75)
No prior constraints	Bayesian probabilistic constraint model	NP	Se 1	0.626	0.218	0.665	0.516-0.773
			Sp 1	0.369	0.217	0.330	0.222-0.476
			Se 2	0.490	0.167	0.495	0.391-0.585
			Sp 2	0.508	0.167	0.503	0.414-0.616
			p	0.498	0.248	0.497	0.305-0.692
	Conditional Covariance Bayesian model	NC	Se 1	0.690	0.183	0.708	0.622-0.807
			Sp 1	0.435	0.226	0.390	0.287-0.568
			Se 2	0.547	0.197	0.535	0.455-0.657
			Sp 2	0.571	0.213	0.562	0.444-0.722
			p	0.553	0.254	0.578	0.366-0.755
Prior constraints	Bayesian probabilistic constraint model	PP	Se 1	0.713	0.036	0.714	0.689-0.738
			Sp 1	0.423	0.051	0.421	0.387-0.456
			Se 2	0.535	0.025	0.535	0.518-0.552
			Sp 2	0.537	0.022	0.537	0.522-0.552
			p	0.538	0.042	0.539	0.510-0.567
	Conditional Covariance Bayesian model	PC	Se 1	0.904	0.037	0.907	0.881-0.931
			Sp 1	0.796	0.119	0.814	0.715-0.892
			Se 2	0.588	0.055	0.580	0.551-0.614
			Sp 2	0.677	0.068	0.677	0.631-0.723
			p	0.649	0.076	0.662	0.608-0.701

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.