Estimation after classification using lot quality assurance sampling: corrections for curtailed sampling with application to evaluating polio vaccination campaigns

Objectives To assess the bias incurred when curtailment of Lot Quality Assurance Sampling (LQAS) is ignored, to present unbiased estimators, to consider the impact of cluster sampling by simulation and to apply our method to published polio immunization data from Nigeria. Methods We present estimators of coverage when using two kinds of curtailed LQAS strategies: semicurtailed and curtailed. We study the proposed estimators with independent and clustered data using three field‐tested LQAS designs for assessing polio vaccination coverage, with samples of size 60 and decision rules of 9, 21 and 33, and compare them to biased maximum likelihood estimators. Lastly, we present estimates of polio vaccination coverage from previously published data in 20 local government authorities (LGAs) from five Nigerian states. Results Simulations illustrate substantial bias if one ignores the curtailed sampling design. Proposed estimators show no bias. Clustering does not affect the bias of these estimators. Across simulations, standard errors show signs of inflation as clustering increases. Neither sampling strategy nor LQAS design influences estimates of polio vaccination coverage in 20 Nigerian LGAs. When coverage is low, semicurtailed LQAS strategies considerably reduces the sample size required to make a decision. Curtailed LQAS designs further reduce the sample size when coverage is high. Conclusions Results presented dispel the misconception that curtailed LQAS data are unsuitable for estimation. These findings augment the utility of LQAS as a tool for monitoring vaccination efforts by demonstrating that unbiased estimation using curtailed designs is not only possible but these designs also reduce the sample size. Objectifs Evaluer les biais induits lorsque la réduction de l’échantillonnage selon la méthode Lot Quality Assurance Sampling (LQAS) est ignorée, présenter des estimateurs non biaisés, examiner l'impact de l’échantillonnage en grappes par simulation et appliquer notre méthode à des données publiées de vaccination du Nigeria. Méthodes Nous présentons des estimateurs de couverture lors de l'utilisation de deux types de stratégie LQAS: semi‐réduit et réduit. Nous étudions les estimateurs proposés avec des données indépendantes et regroupées à l'aide de trois modèles LQAS testés sur le terrain pour évaluer la couverture de la vaccination contre la poliomyélite, avec des échantillons de taille 60 et des règles de décision de 9, 21 et 33, et les comparons aux est