Network model-assisted inference from respondent-driven sampling data
Network model-assisted inference from respondent-driven sampling data
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DOI：
10.1111/rssa.12091
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发表时间：
2015-06-01
影响因子：
2
通讯作者：
Handcock, Mark S.
中科院分区：
文献类型：
--
作者：
Gile, Krista J.;Handcock, Mark S.
受访者驱动的抽样是一种广泛使用的方法，通过对社交网络上的链接进行跟踪，对难以接触到的人群进行抽样。从这样的数据推断需要专门的技术，因为抽样过程既部分超出研究人员的控制，又部分隐含地定义。因此，对于传统的基于设计的推理，通常不可能直接计算抽样权重，而似然推理需要对复杂的抽样过程进行建模。作为另一种选择，我们引入了模型辅助方法，从而产生了利用工作网络模型的基于设计的估计器。我们得到了总体均值的一类新的估计量和相应的Bootstrap标准误差估计量。与现有的估计量相比，我们证明了改进的性能，包括对初始便利样本的调整。我们还将该方法和扩展应用于估计人类免疫缺陷病毒在高危人群中的流行率。
Respondent-driven sampling is a widely used method for sampling hard-to-reach human populations by link tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to compute the sampling weights for traditional design-based inference directly, and likelihood inference requires modelling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared with existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of the prevalence of human immunodeficiency virus in a high-risk population.