TY - JOUR
T1 - Correcting for Partial Verification Bias: A Comparison of Methods
AU - de Groot, Joris A. H.
AU - Janssen, Kristel J. M.
AU - Zwinderman, Aeilko H.
AU - Bossuyt, Patrick M. M.
AU - Reitsma, Johannes B.
AU - Moons, Karel G. M.
PY - 2011
Y1 - 2011
N2 - PURPOSE: 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. Ann Epidemiol 2011;21:139-148. (C) 2011 Elsevier Inc. All rights reserved
AB - PURPOSE: 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. Ann Epidemiol 2011;21:139-148. (C) 2011 Elsevier Inc. All rights reserved
U2 - https://doi.org/10.1016/j.annepidem.2010.10.004
DO - https://doi.org/10.1016/j.annepidem.2010.10.004
M3 - Article
C2 - 21109454
SN - 1047-2797
VL - 21
SP - 139
EP - 148
JO - Annals of epidemiology
JF - Annals of epidemiology
IS - 2
ER -