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Multiple imputation to correct for partial verification bias revisited
Author(s) -
de Groot J. A. H.,
Janssen K. J. M.,
Zwinderman A. H.,
Moons K. G. M.,
Reitsma J. B.
Publication year - 2008
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.3410
Subject(s) - missing data , imputation (statistics) , computer science , statistics , sensitivity (control systems) , data mining , gold standard (test) , standard error , algorithm , mathematics , electronic engineering , engineering
Partial verification refers to the situation where a subset of patients is not verified by the reference (gold) standard and is excluded from the analysis. If partial verification is present, the observed (naive) measures of accuracy such as sensitivity and specificity are most likely to be biased. Recently, Harel and Zhou showed that partial verification can be considered as a missing data problem and that multiple imputation (MI) methods can be used to correct for this bias. They claim that even in simple situations where the verification is random within strata of the index test results, the so‐called Begg and Greenes (B&G) correction method underestimates sensitivity and overestimates specificity as compared with the MI method. However, we were able to demonstrate that the B&G method produces similar results as MI, and that the claimed difference has been caused by a computational error. Additional research is needed to better understand which correction methods should be preferred in more complex scenarios of missing reference test outcome in diagnostic research. Copyright © 2008 John Wiley & Sons, Ltd.