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Comprehensive & Cost Effective Laboratory Monitoring of HIV/AIDS: an African Role Model

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Published on: October 31, 2010

Avoiding verification bias in screening test evaluation in resource poor settings: a case study from Zimbabwe.

Lynne Gaffikin¹, John McGrath, Marc Arbyn

¹JHPIEGO, Baltimore, MD 21231, USA. earthlg@aol.com

Clinical Trials (London, England)

|October 2, 2008

Summary

Verification bias in cervical cancer screening can lead to inaccurate test results. Ensuring random verification of negative tests or 100% verification is crucial for reliable accuracy estimates, especially in low-resource settings.

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Area of Science:

Epidemiology
Biostatistics
Public Health

Background:

Verification bias arises when disease verification rates differ by test results.
Statistical adjustments for verification bias assume data are missing at random (MAR).

Purpose of the Study:

To demonstrate how real-world conditions can violate MAR assumptions for statistical adjustment.
To highlight compromised accuracy due to insufficient sampling of test-negative individuals.

Main Methods:

A two-phase study in Zimbabwe evaluated visual inspection with acetic acid (VIA) for cervical cancer screening.
Phase I: 10% verification of negative tests; Phase II: near-complete verification.
Compared unadjusted, simple-adjusted, and covariate-adjusted estimates; used bootstrap simulations for sampling fraction effects.

Main Results:

Phase I unadjusted sensitivity/specificity: 0.66/0.34. Adjusted estimates suggested the test was useless (0.20/0.80).
Verification capacity was exceeded, compromising random selection of test-negative patients.
Phase II sensitivity/specificity: 0.77/0.64. Over 50% test-negative sampling fraction needed for reliable estimates at 9% prevalence.

Conclusions:

Statistical adjustments failed in Phase I due to unmeasured factors violating MAR.
Standard bias correction formulas are unreliable if MAR is not met.
Low-resource study designs require 100% verification or truly random sampling of test negatives, considering prevalence and sample size.