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首页> 外文期刊>Emerging themes in epidemiology >The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments
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The effect of clustering on lot quality assurance sampling: a probabilistic model to calculate sample sizes for quality assessments

机译：聚类对批次质量保证抽样的影响：一种概率模型，用于计算样本量以进行质量评估

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Background Traditional Lot Quality Assurance Sampling (LQAS) designs assume observations are collected using simple random sampling. Alternatively, randomly sampling clusters of observations and then individuals within clusters reduces costs but decreases the precision of the classifications. In this paper, we develop a general framework for designing the cluster(C)-LQAS system and illustrate the method with the design of data quality assessments for the community health worker program in Rwanda. Results To determine sample size and decision rules for C-LQAS, we use the beta-binomial distribution to account for inflated risk of errors introduced by sampling clusters at the first stage. We present general theory and code for sample size calculations. The C-LQAS sample sizes provided in this paper constrain misclassification risks below user-specified limits. Multiple C-LQAS systems meet the specified risk requirements, but numerous considerations, including per-cluster versus per-individual sampling costs, help identify optimal systems for distinct applications. Conclusions We show the utility of C-LQAS for data quality assessments, but the method generalizes to numerous applications. This paper provides the necessary technical detail and supplemental code to support the design of C-LQAS for specific programs.
机译：背景技术传统的批次质量保证抽样（LQAS）设计假定使用简单的随机抽样来收集观察数据。备选地，随机采样观察值的集群，然后对集群中的个体进行采样，可以降低成本，但会降低分类的精度。在本文中，我们开发了一个用于设计cluster（C）-LQAS系统的通用框架，并通过设计数据质量评估为卢旺达社区卫生工作者计划说明了该方法。结果为了确定C-LQAS的样本量和决策规则，我们使用β-二项分布来考虑在第一阶段由抽样群集引入的虚假错误风险。我们介绍了用于样本量计算的一般理论和代码。本文提供的C-LQAS样本数量将误分类风险限制在用户指定的限制以下。多个C-LQAS系统可以满足指定的风险要求，但是要考虑的各种因素（包括每个集群与每个个体的采样成本）有助于确定针对不同应用的最佳系统。结论我们展示了C-LQAS在数据质量评估中的实用性，但该方法可推广到许多应用。本文提供了必要的技术细节和补充代码，以支持针对特定程序的C-LQAS设计。

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