Correcting for partial verification bias: a comparison of methods.
OBJECTIVE
A common problem in diagnostic research is that the reference standard has not been carried out in all patients. This partial verification may lead to biased accuracy measures of the test under study. The authors studied the performance of multiple imputation and the conventional correction method proposed by Begg and Greenes under a range of different situations of partial verification.


METHODS
In a series of simulations, using a previously published deep venous thrombosis data set (n = 1292), the authors set the outcome of the reference standard to missing based on various underlying mechanisms and by varying the total number of missing values. They then compared the performance of the different correction methods.


RESULTS
The results of the study show that when the mechanism of missing reference data is known, accuracy measures can easily be correctly adjusted using either the Begg and Greenes method, or multiple imputation. In situations where the mechanism of missing reference data is complex or unknown, we recommend using multiple imputation methods to correct.


CONCLUSIONS
These methods can easily apply for both continuous and categorical variables, are readily available in statistical software and give reliable estimates of the missing reference data.
Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, The Netherlands. J.degroot-17@umcutrecht.nl
Joris A H
JA
de Groot
Kristel J M
KJ
Janssen
Aeilko H
AH
Zwinderman
Patrick M M
PM
Bossuyt
Johannes B
JB
Reitsma
Karel G M
KG
Moons
D015982:Bias; D003627:Data Interpretation, Statistical; D004812:Epidemiologic Methods; D008962:Models, Theoretical; D012016:Reference Values