Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Division of HIV, STD and TB Services, New Jersey Department of Health, Trenton, NJ, USA.
Ann Epidemiol. 2024 Jun;94:100-105. doi: 10.1016/j.annepidem.2024.05.001. Epub 2024 May 6.
Respondent-driven sampling (RDS) is widely used to sample populations with higher risk of HIV infection for whom no sampling frames exist. However, few studies have been done to assess the reliability of RDS in real world settings.
We assessed the reliability of naïve RDS samples using five rounds of the National HIV Behavioral Surveillance - People Who Inject Drugs surveys in Newark, New Jersey from 2005 to 2018. Specifically, we compared the distributions of time-insensitive demographic characteristics in temporally adjacent RDS samples with Monte Carlo Two-Sample Kolmogorov-Smirnov Test with 100,000 replicates. The distributions of time-sensitive demographic characteristics were also compared as sensitivity analyses.
The study showed that repeated RDS samples among people who inject drugs in the greater Newark area, New Jersey were reliable in most of time-insensitive demographics and recruitment homophily statistics. Sensitivity analyses of time-sensitive demographics also presented consistencies in most of temporally adjacent samples.
In conclusion, RDS has the potential to provide reliable samples, but demographic characteristics of RDS samples may be easily biased by homophily. Future studies using RDS may need to pay more attention to potential homophily bias and consider necessary diagnostic procedures and sample adjustments.
响应驱动抽样(RDS)被广泛用于对没有抽样框架的高危 HIV 感染人群进行抽样。然而,很少有研究评估 RDS 在实际环境中的可靠性。
我们使用 2005 年至 2018 年在新泽西州纽瓦克进行的五次全国艾滋病毒行为监测-注射毒品者调查的原始 RDS 样本评估了其可靠性。具体来说,我们使用 100000 次重复的蒙特卡罗两样本 Kolmogorov-Smirnov 检验比较了时间敏感和不敏感人口统计特征在时间相邻的 RDS 样本中的分布。作为敏感性分析,还比较了时间敏感人口统计特征的分布。
研究表明,新泽西州纽瓦克大都市区的注射毒品者中重复的 RDS 样本在大多数时间不敏感的人口统计学和招募同质性统计数据中是可靠的。时间敏感人口统计数据的敏感性分析在大多数时间相邻的样本中也呈现出一致性。
总之,RDS 有潜力提供可靠的样本,但 RDS 样本的人口统计学特征可能容易受到同质性的偏差影响。未来使用 RDS 的研究可能需要更加注意潜在的同质性偏差,并考虑必要的诊断程序和样本调整。