Table 8.
Bayesian latent class Analysis of MAT and ELISA data from using rLipL32, rLipL41, rLigA-Rep, and rLigB-Rep in a 1:1:1:1 mixture
| Mean | SD | MC_error | Md | 95% credible interval | |
|---|---|---|---|---|---|
| π | 0.821 | 0.0454 | 2.011E-4 | 0.8208 | (0.730, 0.908) |
| S1 | 0.902 | 0.0361 | 1.800E-4 | 0.9043 | (0.828, 0.965) |
| S2 | 0.789 | 0.0415 | 1.846E-4 | 0.788 | (0.710, 0.872) |
| C1 | 0.819 | 0.0935 | 3.229E-4 | 0.8308 | (0.609, 0.963) |
| C2 | 0.868 | 0.0671 | 2.164E-4 | 0.8761 | (0.717, 0.973) |
| ρp | 0.397 | 0.1594 | 7.246E-4 | 0.4197 | (0.026, 0.653) |
| ρn | 0.289 | 0.2706 | 8.49E-4 | 0.2725 | (−0.155, 0.826) |
IgM = immunoglobulin M; IgG = immunoglobulin G; MAT = microscopic agglutination test; MC_error = Monte Carlo error; Md = median; SD = standard deviations. π is prevalence of the disease; S1 is the sensitivity of MAT; S2 is the sensitivity of ELISA; C1 is the specificity of MAT; C2 is the specificity of ELISA; ρp is the correlation between the two tests with the disease population; ρn is correlation between the two tests with the non-disease population.