Department of Epidemiology, University of North Carolina, Chapel Hill, NC 27599-7435, USA.
Am J Epidemiol. 2010 Mar 15;171(6):674-7; discussion 678-81. doi: 10.1093/aje/kwp436. Epub 2010 Feb 5.
Positivity, or the experimental treatment assignment assumption, requires that there be both exposed and unexposed participants at every combination of the values of the observed confounders in the population under study. Positivity is essential for inference but is often overlooked in practice by epidemiologists. This issue of the Journal includes 2 articles featuring discussions related to positivity. Here the authors define positivity, distinguish between deterministic and random positivity, and discuss the 2 relevant papers in this issue. In addition, the commentators illustrate positivity in simple 2 x 2 tables, as well as detail some ways in which epidemiologists may examine their data for nonpositivity and deal with violations of positivity in practice.
阳性,或实验处理分配假设,要求在研究人群中每个观察到的混杂因素值的组合中都有暴露和未暴露的参与者。阳性是推断的基础,但在实践中经常被流行病学家忽视。本期杂志包含 2 篇文章,讨论了与阳性相关的问题。在这里,作者定义了阳性,区分了确定性阳性和随机阳性,并讨论了本期杂志中的 2 篇相关论文。此外,评论员在简单的 2x2 表中说明了阳性,并详细介绍了流行病学家在实践中可能检查数据是否存在非阳性和处理阳性违反的一些方法。