Partial verification bias correction using scaled inverse probability resampling for binary diagnostic tests.

ABSTRACT: Diagnostic accuracy studies are crucial for evaluating new tests before their clinical application. These tests are compared with gold standard tests, and accuracy measures such as sensitivity (Sn) and specificity (Sp) are often calculated. However, these studies frequently suffer from partial verification bias (PVB) due to selective verification of patients, which leads to biased accuracy estimates. Among the methods for correcting PVB under the missing-at-random assumption for binary diagnostic tests, a bootstrap-based method known as the inverse probability bootstrap (IPB) was proposed. IPB demonstrated low bias for estimating Sn and Sp, but exhibited higher standard errors (SE) than other PVB correction methods and only corrected the distribution of the verified portion of the PVB data

SUBMITTER: Arifin WN 

PROVIDER: S-EPMC12469146 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature