Improved Inference for Respondent-Driven Sampling Data With Application to HIV Prevalence Estimation
受访者驱动抽样的改进推断及 HIV 患病率估计应用AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要明确研究网络链式抽样，并分析网络连接度差异对总体估计的影响。
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受访者驱动抽样的改进推断及 HIV 患病率估计应用
- 研究问题
- 网络链式抽样中，有放回近似如何受到样本比例与连接度差异影响，尊重无放回机制能否改善总体均值估计？
- 方法与思路
- 作者将受访者驱动抽样表示为逐次抽样，构造总体均值估计量，并对隐藏总体规模未知的情形进行敏感性分析。
- 材料陈述的贡献
- 摘要称，新估计量在总体规模已知时表现更好，小样本比例时接近现有表示，全总体抽样时回到样本均值。
- 阅读关注点
- 应核对逐次抽样近似与实际招募机制的关系、总体规模和连接度信息的使用，以及初始样本选择引起的偏差。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，优势依赖总体规模等条件；作者明确指出初始样本选择仍可能导致偏差。
原文摘要
Respondent-driven sampling is a form of link-tracing network sampling, which is widely used to study hard-to-reach populations, often to estimate population proportions. Previous treatments of this process have used a with-replacement approximation, which we show induces bias in estimates for large sample fractions and differential network connectedness by characteristic of interest. We present a treatment of respondent-driven sampling as a successive sampling process. Unlike existing representations, our approach respects the essential without-replacement feature of the process, while converging to an existing with-replacement representation for small sample fractions, and to the sample mean for a full-population sample. We present a successive-sampling based estimator for population means based on respondent-driven sampling data, and demonstrate its superior performance when the size of the hidden population is known. We present sensitivity analyses for unknown population sizes. In addition, we note that like other existing estimators, our new estimator is subject to bias induced by the selection of the initial sample. Using data collected among three populations in two countries, we illustrate the application of this approach to populations with varying characteristics. We conclude that the successive sampling estimator improves on existing estimators, and can also be used as a diagnostic tool when population size is not known. This article has supplementary material online.