LQAS-IMP

LQAS-IMP modifies Lot Quality Assurance Sampling to incorporate imperfect diagnostic test sensitivity and specificity for valid classification of prevalence-based health indicators.

Key Features:

• Adaptation for Imperfect Tests: Modifies standard LQAS procedures to account for diagnostic test sensitivity and specificity, ensuring classification errors remain within user-specified limits.
• Error Correction: Derives a modified procedure that incorporates test sensitivity and specificity into system design to correct inflation of classification errors caused by imperfect tests.
• System Design Adjustment: Integrates sensitivity and specificity into the calculation of sample size and decision rules to yield valid classification errors.
• Application Case: Applied to COVID-19 serosurveillance among healthcare workers at Zanmi Lasante in Haiti using antibody tests with imperfect diagnostic accuracy.

Scientific Applications:

• Health indicator monitoring: Enables classification of areas or populations for prevalence-based indicators in low-resource settings where tests are imperfect.
• Serosurveillance: Supports serosurveys for COVID-19 antibodies to assess prior circulation among healthcare workers using limited antibody tests.
• Prevalence estimation and inference: Facilitates valid prevalence-related inferences when diagnostic sensitivity and specificity are less than perfect.

Methodology:

Derives a modified LQAS procedure that explicitly incorporates diagnostic test sensitivity and specificity into system design to compute adjusted sample size and decision rules that maintain user-specified classification error rates.