Imperial College London
Alternate

Professor Toby Prevost

Faculty of Medicine, School of Public Health

Visiting Professor
 
 
 
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Contact

 

a.prevost

 
 
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Location

 

57Stadium HouseWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Day:2022:10.1186/s12874-021-01481-w,
author = {Day, E and Eldred-Evans, D and Prevost, AT and Hashim, U A and Fiorentino, F},
doi = {10.1186/s12874-021-01481-w},
journal = {BMC Medical Research Methodology},
title = {Adjusting for verification bias in diagnostic accuracy measures when comparing multiple screening 2 tests - an application to the IP1-PROSTAGRAM study},
url = {http://dx.doi.org/10.1186/s12874-021-01481-w},
volume = {22},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Introduction Novel screening tests used to detect a target condition are compared against either a reference standard or other existing screening methods. However, as it is not always possible to apply the reference standard on the whole population under study, verification bias is introduced. Statistical methods exist to adjust estimates to account for this bias. We extend common methods to adjust for verification bias when multiple tests are compared to a reference standard using data from a prospective double blind screening study for prostate cancer. Methods Begg and Greenes method and multiple imputation are extended to include the results of multiple screening tests which determine condition verification status. These two methods are compared to the complete case analysis using the IP1-PROSTAGRAM study data. IP1-PROSTAGRAM used a paired84 cohort double-blind design to evaluate the use of imaging as alternative tests to screen for prostate85 cancer, compared to a blood test called prostate specific antigen (PSA). Participants with positive imaging (index) and/or PSA (control) underwent a prostate biopsy (reference). Results When comparing complete case results to Begg and Greenes and methods of multiple imputation there is a statistically significant increase in the specificity estimates for all screening tests. Sensitivity estimates remained similar across the methods, with completely overlapping 95% confidence intervals. Negative predictive value (NPV) estimates were higher when adjusting for verification bias, compared to complete case analysis, even though the 95% confidence intervals overlap. Positive predictive value (PPV) estimates were similar across all methods. Conclusion Statistical methods are required to adjust for verification bias in accuracy estimates of screening tests. Expanding Begg and Greenes method to include multiple screening tests can be computationally intensive, hence multiple imputation is recommended, especially as it can be modifie
AU  - Day,E
AU  - Eldred-Evans,D
AU  - Prevost,AT
AU  - Hashim,U A
AU  - Fiorentino,F
DO  - 10.1186/s12874-021-01481-w
PY  - 2022///
SN  - 1471-2288
TI  - Adjusting for verification bias in diagnostic accuracy measures when comparing multiple screening 2 tests - an application to the IP1-PROSTAGRAM study
T2  - BMC Medical Research Methodology
UR  - http://dx.doi.org/10.1186/s12874-021-01481-w
UR  - http://hdl.handle.net/10044/1/93088
VL  - 22
ER  -
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