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Optimal design of multiple-objective Lot Quality Assurance Sampling (LQAS) plans.

Belmiro P M Duarte1,2, Weng Kee Wong3

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Summary

This study introduces a new method for designing Lot Quality Assurance Sampling (LQAS) plans, enhancing health monitoring accuracy. The approach optimizes plans for diagnostic accuracy metrics, improving public health surveillance effectiveness.

Keywords:
Lot Quality Assurance SamplingMixed Integer Nonlinear Programmingdiagnostic measuresdisease monitoring programsoptimal designpublic health policy

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Area of Science:

  • Health Surveillance
  • Biostatistics
  • Public Health

Background:

  • Lot Quality Assurance Sampling (LQAS) is a standard method for health monitoring.
  • Existing LQAS plans primarily focus on error rates, potentially limiting their utility for specific diagnostic accuracy targets.

Purpose of the Study:

  • To develop a systematic approach for designing multiple-objective LQAS plans.
  • To incorporate user-specified error rates and diagnostic accuracy metrics (sensitivity, specificity, PPV, NPV) into LQAS plan design.
  • To offer flexibility in generating both classic and optimized LQAS plans.

Main Methods:

  • Utilizing Mixed Integer Nonlinear Programming (MINLP) tools for methodology implementation.
  • Designing LQAS plans to meet defined type 1 and type 2 error rates.
  • Optimizing plans for various diagnostic accuracy metrics across different prevalence scenarios.

Main Results:

  • The proposed approach successfully generates classic LQAS plans controlling error rates.
  • The methodology identifies optimal LQAS plans that simultaneously satisfy multiple diagnostic metric objectives.
  • Demonstrated application using a malaria indicator survey in Mozambique.

Conclusions:

  • The novel systematic approach enhances the design of LQAS plans for health monitoring.
  • This flexible methodology allows for tailored plans meeting diverse user needs and diagnostic accuracy targets.
  • The MINLP-based approach provides a robust framework for optimizing public health surveillance tools.