College of Public Health, The Ohio State University, Columbus, OH, USA.
Int J Epidemiol. 2010 Feb;39(1):60-8. doi: 10.1093/ije/dyn366.
Researchers around the world are using Lot Quality Assurance Sampling (LQAS) techniques to assess public health parameters and evaluate program outcomes. In this paper, we report that there are actually two methods being called LQAS in the world today, and that one of them is badly flawed.
This paper reviews fundamental LQAS design principles, and compares and contrasts the two LQAS methods. We raise four concerns with the simply-written, freely-downloadable training materials associated with the second method.
The first method is founded on sound statistical principles and is carefully designed to protect the vulnerable populations that it studies. The language used in the training materials for the second method is simple, but not at all clear, so the second method sounds very much like the first. On close inspection, however, the second method is found to promote study designs that are biased in favor of finding programmatic or intervention success, and therefore biased against the interests of the population being studied.
We outline several recommendations, and issue a call for a new high standard of clarity and face validity for those who design, conduct, and report LQAS studies.
世界各地的研究人员正在使用批量质量保证抽样 (LQAS) 技术来评估公共卫生参数和评估计划结果。在本文中，我们报告说，当今世界实际上有两种被称为 LQAS 的方法，其中一种方法存在严重缺陷。
本文回顾了基本的 LQAS 设计原则，并比较和对比了这两种 LQAS 方法。我们对第二种方法的简单编写、免费下载的培训材料提出了四个关注问题。
第一种方法基于可靠的统计原则，并精心设计，以保护其研究的弱势群体。第二种方法培训材料中的语言简单明了，但并不完全清楚，因此第二种方法听起来非常类似于第一种方法。然而，仔细检查后发现，第二种方法倾向于促进有利于发现计划或干预成功的研究设计，因此不利于被研究人群的利益。
我们概述了一些建议，并呼吁那些设计、进行和报告 LQAS 研究的人提高设计的清晰度和表面有效性的新标准。
