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Methods for assessing the accuracy of PCR‐based tests: comparisons and extensions
Author(s) -
Ou SanSan,
Hughes James P.,
Richardson Barbra A.
Publication year - 2005
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2003
Subject(s) - estimator , parametric statistics , sensitivity (control systems) , covariate , computer science , statistics , parametric model , mycoplasma genitalium , mathematics , biology , virology , electronic engineering , engineering , chlamydia trachomatis
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 . Copyright © 2004 John Wiley & Sons, Ltd.