A robust method using propensity score stratification for correcting verification bias for binary tests.

ABSTRACT: Sensitivity and specificity are common measures of the accuracy of a diagnostic test. The usual estimators of these quantities are unbiased if data on the diagnostic test result and the true disease status are obtained from all subjects in an appropriately selected sample. In some studies, verification of the true disease status is performed only for a subset of subjects, possibly depending on the result of the diagnostic test and other characteristics of the subjects. Estimators of sensitivity and specificity based on this subset of subjects are typically biased; this is known as verification bias. Methods have been proposed to correct verification bias under the assumption that the missing data on disease status are missing at random (MAR), that is, the probability of missingness depends

SUBMITTER: He H 

PROVIDER: S-EPMC3276270 | biostudies-literature | 2012 Jan

REPOSITORIES: biostudies-literature