Optimal design of multiple‐objective Lot Quality Assurance Sampling (LQAS) plans. Issue 2 (1st April 2019)

• Record Type: Journal Article
• Authors: Duarte, Belmiro P.M.; Wong, Weng Kee
• Is Part Of: Biometrics. Volume 75:Issue 2(2019)
• Page Start: 572
• Page End: 581
• Publication Date: 2019-04-01
• Subjects: diagnostic measures -- disease monitoring programs -- Lot Quality Assurance Sampling -- Mixed Integer Nonlinear Programming -- optimal design -- public health policy; Biometry -- Periodicals; 570.15195
• DOI: 10.1111/biom.13008
• Languages: English
• ISSNs: 0006-341X

Abstract

Abstract: Lot Quality Assurance Sampling (LQAS) plans are widely used for health monitoring purposes. We propose a systematic approach to design multiple‐objective LQAS plans that meet user‐specified type 1 and 2 error rates and targets for selected diagnostic accuracy metrics. These metrics may include sensitivity, specificity, positive predictive value, and negative predictive value in high or low 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 generate classic LQAS plans that control error rates only and find optimal LQAS plans that meet multiple objectives in terms of diagnostic metrics. We give examples, compare results with the classic LQAS and provide an application using a malaria outcome indicator survey in Mozambique.