Skip to main content

Table 3 Example of data generated under the setting of a screening study with 5000 participants and the underlying incidence of disease being 10%

From: Statistical methods to correct for verification bias in diagnostic studies are inadequate when there are few false negatives: a simulation study

  (1) (2)    
Data Set Number with negative diagnostic test and verified Number of false negatives Proportion of false negatives (2)/(1) AUC
     Categorize in 10 bins Alonzo-Pepe
Fully verified 2500 99 4.0% 0.750 0.750
With verification bias      
Replication 1 50 (2.0%) 2 4.0% 0.690 0.712
Replication 2 64 (2.6%) 4 6.3% 0.852 0.857
Replication 3 40 (1.6%) 2 5.0% 0.550 0.546
Replication 4 61 (2.4%) 0 0.0% 0.812 0.826
Replication 5 65 (2.6%) 3 4.6% 0.790 0.803
  1. In the fully verified data set, definitive test results were known for all 5000 participants. In the replications with verification bias, only 500 (10%) participants underwent definitive testing. False negatives are defined as verified participants with a diagnostic test result less than the median of the diagnostic test results and with a positive gold standard result. The AUC of the fully verified data set was 0.750; all other estimates are with correction for verification bias.