TY - JOUR
T1 - Adjusting for Partial Verification or Workup Bias in Meta-Analyses of Diagnostic Accuracy Studies
AU - de Groot, Joris A. H.
AU - Dendukuri, Nandini
AU - Janssen, Kristel J. M.
AU - Reitsma, Johannes B.
AU - Brophy, James
AU - Joseph, Lawrence
AU - Bossuyt, Patrick M. M.
AU - Moons, Karel G. M.
PY - 2012
Y1 - 2012
N2 - A key requirement in the design of diagnostic accuracy studies is that all study participants receive both the test under evaluation and the reference standard test. For a variety of practical and ethical reasons, sometimes only a proportion of patients receive the reference standard, which can bias the accuracy estimates. Numerous methods have been described for correcting this partial verification bias or workup bias in individual studies. In this article, the authors describe a Bayesian method for obtaining adjusted results from a diagnostic meta-analysis when partial verification or workup bias is present in a subset of the primary studies. The method corrects for verification bias without having to exclude primary studies with verification bias, thus preserving the main advantages of a meta-analysis: increased precision and better generalizability. The results of this method are compared with the existing methods for dealing with verification bias in diagnostic meta-analyses. For illustration, the authors use empirical data from a systematic review of studies of the accuracy of the immunohistochemistry test for diagnosis of human epidermal growth factor receptor 2 status in breast cancer patients
AB - A key requirement in the design of diagnostic accuracy studies is that all study participants receive both the test under evaluation and the reference standard test. For a variety of practical and ethical reasons, sometimes only a proportion of patients receive the reference standard, which can bias the accuracy estimates. Numerous methods have been described for correcting this partial verification bias or workup bias in individual studies. In this article, the authors describe a Bayesian method for obtaining adjusted results from a diagnostic meta-analysis when partial verification or workup bias is present in a subset of the primary studies. The method corrects for verification bias without having to exclude primary studies with verification bias, thus preserving the main advantages of a meta-analysis: increased precision and better generalizability. The results of this method are compared with the existing methods for dealing with verification bias in diagnostic meta-analyses. For illustration, the authors use empirical data from a systematic review of studies of the accuracy of the immunohistochemistry test for diagnosis of human epidermal growth factor receptor 2 status in breast cancer patients
U2 - https://doi.org/10.1093/aje/kwr383
DO - https://doi.org/10.1093/aje/kwr383
M3 - Article
C2 - 22422923
SN - 0002-9262
VL - 175
SP - 847
EP - 853
JO - American journal of epidemiology
JF - American journal of epidemiology
IS - 8
ER -