利用调查对象驱动的抽样研究的连续数据推断双变量关联。

IF 1 4区 数学 Q3 STATISTICS & PROBABILITY
Samantha Malatesta, Karen R Jacobson, Tara Carney, Eric D Kolaczyk, Krista J Gile, Laura F White
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引用次数: 0

摘要

被调查者驱动抽样(RDS)是一种链接追踪抽样设计,旨在从隐藏人群中进行抽样。尽管流行病学研究对变量之间的关联非常感兴趣,但通过RDS收集的变量之间的关系进行推断的统计工作很少。链接追踪设计与同质性相结合,即人们倾向于与有共同特征的人建立联系,从而在有联系的个体之间产生相似性。这种依赖性放大了传统统计方法(如t检验、回归等)的第一类误差。建立了双变量关联的半参数随机化检验来检验两个分类变量之间的关联。我们直接扩展了这项工作,并提出了两个变量之间关系的半参数随机化检验,当一个或两个变量是连续的。我们将我们的方法应用于变量,这些变量对于了解南非伍斯特的非法吸毒者之间的结核病流行病学很重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Inferring bivariate associations with continuous data from studies using respondent-driven sampling.

Respondent-driven sampling (RDS) is a link-tracing sampling design that was developed to sample from hidden populations. Although associations between variables are of great interest in epidemiological research, there has been little statistical work on inference on relationships between variables collected through RDS. The link-tracing design, combined with homophily, the tendency for people to connect to others with whom they share characteristics, induces similarity between linked individuals. This dependence inflates the Type 1 error of conventional statistical methods (e.g. t-tests, regression, etc.). A semiparametric randomization test for bivariate association was developed to test for association between two categorical variables. We directly extend this work and propose a semiparametric randomization test for relationships between two variables, when one or both are continuous. We apply our method to variables that are important for understanding tuberculosis epidemiology among people who smoke illicit drugs in Worcester, South Africa.

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来源期刊
CiteScore
2.50
自引率
0.00%
发文量
76
审稿时长
>12 weeks
期刊介绍: The Journal of the Royal Statistical Society, Series C (Applied Statistics) is a journal of international repute for statisticians both inside and outside the academic world. The journal is concerned with papers which deal with novel solutions to real life statistical problems by adapting or developing methodology, or by demonstrating the proper application of new or existing statistical methods to them. At their heart therefore the papers in the journal are motivated by examples and statistical data of all kinds. The subject-matter covers the whole range of inter-disciplinary fields, e.g. applications in agriculture, genetics, industry, medicine and the physical sciences, and papers on design issues (e.g. in relation to experiments, surveys or observational studies). A deep understanding of statistical methodology is not necessary to appreciate the content. Although papers describing developments in statistical computing driven by practical examples are within its scope, the journal is not concerned with simply numerical illustrations or simulation studies. The emphasis of Series C is on case-studies of statistical analyses in practice.
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