Gile Krista J, Handcock Mark S
University of Massachusetts, Amherst, MA, USA. University of California at Los Angeles, Los Angeles, CA, USA.
J R Stat Soc Ser A Stat Soc. 2015 Jun;178(3):619-639. doi: 10.1111/rssa.12091. Epub 2015 Jan 27.
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 directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling 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 to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.
应答者驱动抽样是一种广泛使用的方法，通过在社会网络中进行链接追踪来对难以接触到的人群进行抽样。从这类数据进行推断需要专门的技术，因为抽样过程部分超出了研究者的控制范围，部分是隐含定义的。因此，通常不可能直接计算传统基于设计的推断的抽样权重，而似然推断需要对复杂的抽样过程进行建模。作为一种替代方法，我们引入了一种模型辅助方法，从而得到一种利用工作网络模型的基于设计的估计量。我们推导了一类新的总体均值估计量和相应的自助标准误差估计量。我们证明了与现有估计量相比性能有所提高，包括对初始便利样本的调整。我们还将该方法及其扩展应用于高危人群中艾滋病毒流行率的估计。
