Table 1 Summary of the proposed statistical models, their assumptions regarding the diagnostic method, and the additional data required to fit these models

Standard logistic regression

None

Perfect detection (i.e., sensitivity and specificity equal to 100 %)

Bayesian model 1

Estimate of sensitivity \(\widehat{SN}\) and specificity \(\widehat{SP}\) based on external study

Sensitivity and specificity are perfectly known constants, equal to the estimates from external study

Bayesian model 2

Data on sensitivity and specificity (i.e., \(N_{ + } ,T_{ + } ,N_{ - } ,T_{ - }\)) from external study

Sensitivity and specificity are constants and external study provides reasonable prior information on sensitivity and specificity for the target study

Bayesian model 3

Subset of individuals diagnosed with the regular and the gold standard method

Sensitivity and specificity can vary as a function of covariates. This model does not rely on data from external study (i.e., does not rely on transportability assumption)