UCLA

Lot Quality Assurance Sampling (LQAS) plans are widely used for health monitoringpurposes. We propose a systematic approach to design multiple-objective LQAS plans that meet userspecifiedtype 1 and 2 error rates and targets for selected diagnostic accuracy metrics. These metricsmay include sensitivity, specificity, positive predictive value, and negative predictive value in high orlow anticipated prevalence rate populations. We use Mixed Integer Nonlinear Programming (MINLP)tools to implement our design methodology. Our approach is flexible in that it can directly generateclassic LQAS plans that control error rates only and find optimal LQAS plans that meet multipleobjectives in terms of diagnostic metrics. We give examples, compare results with the classic LQASand provide an application using a malaria outcome indicator survey in Mozambique.