Estimating Design Effect and Calculating Sample Size for Respondent-Driven Sampling Studies of Injection Drug Users in the United States
Estimating Design Effect and Calculating Sample Size for Respondent-Driven Sampling Studies of Injection Drug Users in the United States
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DOI：
10.1007/s10461-012-0147-8
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发表时间：
2012-05-01
影响因子：
4.4
通讯作者：
DiNenno, Elizabeth
中科院分区：
文献类型：
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作者：
Wejnert, Cyprian;Huong Pham;DiNenno, Elizabeth
受访者驱动抽样 (RDS) 在对隐藏人群（包括注射吸毒者 (IDU)）进行抽样时变得越来越流行。然而，RDS 数据是独一无二的，需要专门的分析技术，其中许多技术尚未开发。 RDS 样本量估计需要了解设计效果 (DE)，而设计效果只能事后计算。很少有研究使用现实世界的经验数据来分析 RDS DE。我们分析了使用标准化协议收集的 43 个 IDU 样本的估计 DE。我们发现之前关于样本量至少加倍的建议与 DE = 2 一致，低估了真实的 DE，并建议研究人员在计算样本量时使用 DE = 4 作为替代估计值。提出了注射吸毒者 RDS 研究样本量的计算公式。资源有限的研究人员可能希望接受稍高的标准误差，以保持较低的样本量要求。我们的结果强调了在分析中忽视抽样设计的危险。
Respondent-driven sampling (RDS) has become increasingly popular for sampling hidden populations, including injecting drug users (IDU). However, RDS data are unique and require specialized analysis techniques, many of which remain underdeveloped. RDS sample size estimation requires knowing design effect (DE), which can only be calculated post hoc. Few studies have analyzed RDS DE using real world empirical data. We analyze estimated DE from 43 samples of IDU collected using a standardized protocol. We find the previous recommendation that sample size be at least doubled, consistent with DE = 2, underestimates true DE and recommend researchers use DE = 4 as an alternate estimate when calculating sample size. A formula for calculating sample size for RDS studies among IDU is presented. Researchers faced with limited resources may wish to accept slightly higher standard errors to keep sample size requirements low. Our results highlight dangers of ignoring sampling design in analysis.