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2
Evaluation of respondent-driven sampling.回应者驱动抽样评估。
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3
Closed-form confidence intervals for functions of the normal mean and standard deviation.正态均值和标准差函数的闭式置信区间。
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4
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J Urban Health. 2010 Sep;87(5):839-50. doi: 10.1007/s11524-010-9486-9.
5
Assessing respondent-driven sampling.评估受访者驱动抽样法。
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6
Possible increase in HIV and syphilis prevalence among men who have sex with men in Guangzhou, China: results from a respondent-driven sampling survey.中国广州男男性行为者中艾滋病毒和梅毒感染率可能上升：基于应答者驱动抽样调查的结果。
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7
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在使用应答者驱动抽样的研究中对效应量度的估计

Towards the estimation of effect measures in studies using respondent-driven sampling.

作者信息

Rotondi Michael A

机构信息

York University, Toronto, Ontario, Canada,

出版信息

J Urban Health. 2014 Jun;91(3):592-7. doi: 10.1007/s11524-013-9836-5.

DOI:10.1007/s11524-013-9836-5
PMID:24327167
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4074320/
Abstract

Respondent-driven sampling (RDS) is an increasingly common sampling technique to recruit hidden populations. Statistical methods for RDS are not straightforward due to the correlation between individual outcomes and subject weighting; thus, analyses are typically limited to estimation of population proportions. This manuscript applies the method of variance estimates recovery (MOVER) to construct confidence intervals for effect measures such as risk difference (difference of proportions) or relative risk in studies using RDS. To illustrate the approach, MOVER is used to construct confidence intervals for differences in the prevalence of demographic characteristics between an RDS study and convenience study of injection drug users. MOVER is then applied to obtain a confidence interval for the relative risk between education levels and HIV seropositivity and current infection with syphilis, respectively. This approach provides a simple method to construct confidence intervals for effect measures in RDS studies. Since it only relies on a proportion and appropriate confidence limits, it can also be applied to previously published manuscripts.

摘要

应答者驱动抽样（RDS）是一种用于招募隐藏人群的越来越常见的抽样技术。由于个体结果与受试者权重之间的相关性，RDS的统计方法并不简单；因此，分析通常仅限于总体比例的估计。本手稿应用方差估计恢复方法（MOVER）为使用RDS的研究中的效应量（如风险差（比例差）或相对风险）构建置信区间。为了说明该方法，MOVER用于为注射吸毒者的RDS研究和便利抽样研究之间人口统计学特征患病率的差异构建置信区间。然后应用MOVER分别获得教育水平与HIV血清阳性和梅毒现感染之间相对风险的置信区间。这种方法为RDS研究中的效应量构建置信区间提供了一种简单的方法。由于它仅依赖于一个比例和适当的置信限，它也可以应用于以前发表的手稿。