Methods for assessing the accuracy of PCR-based tests: comparisons and extensions

Polymerase chain reaction (PCR) based tests are commonly used to diagnose various infections. Such tests are assumed to be highly 'sensitive', however, no consensus definition of, or method for estimating, sensitivity exists. Hughes and Totten proposed that sensitivity be defined as a function of the number of target DNA molecules in the sample with specificity corresponding to the case where there is no target DNA molecule present. They then developed parametric, non-parametric and semi-parametric models for estimating the sensitivity curve. In this paper a general model is proposed that yields their three models as special cases when specificity is assumed to be 1.0. We also extend the general model to incorporate covariates. Simulation studies are used to compare the different estimators. The methods are applied to data from a PCR-based test for Mycoplasma genitalium.