McCreesh Nicky, Copas Andrew, Seeley Janet, Johnston Lisa G, Sonnenberg Pam, Hayes Richard J, Frost Simon D W, White Richard G
School of Medicine, Pharmacy and Health, Durham University, Durham, United Kingdom.
PLoS One. 2013 Oct 31;8(10):e78402. doi: 10.1371/journal.pone.0078402. eCollection 2013.
Respondent-driven sampling (RDS) is a variant of a link-tracing design intended for generating unbiased estimates of the composition of hidden populations that typically involves giving participants several coupons to recruit their peers into the study. RDS may generate biased estimates if coupons are distributed non-randomly or if potential recruits present for interview non-randomly. We explore if biases detected in an RDS study were due to either of these mechanisms, and propose and apply weights to reduce bias due to non-random presentation for interview.
Using data from the total population, and the population to whom recruiters offered their coupons, we explored how age and socioeconomic status were associated with being offered a coupon, and, if offered a coupon, with presenting for interview. Population proportions were estimated by weighting by the assumed inverse probabilities of being offered a coupon (as in existing RDS methods), and also of presentation for interview if offered a coupon by age and socioeconomic status group.
Younger men were under-recruited primarily because they were less likely to be offered coupons. The under-recruitment of higher socioeconomic status men was due in part to them being less likely to present for interview. Consistent with these findings, weighting for non-random presentation for interview by age and socioeconomic status group greatly improved the estimate of the proportion of men in the lowest socioeconomic group, reducing the root-mean-squared error of RDS estimates of socioeconomic status by 38%, but had little effect on estimates for age. The weighting also improved estimates for tribe and religion (reducing root-mean-squared-errors by 19-29%), but had little effect for sexual activity or HIV status.
Data collected from recruiters on the characteristics of men to whom they offered coupons may be used to reduce bias in RDS studies. Further evaluation of this new method is required.
应答驱动抽样（RDS）是一种链式追踪设计的变体，旨在对隐性人群的构成进行无偏估计，通常做法是给参与者若干优惠券，让他们招募同伴参与研究。如果优惠券分发不随机，或者潜在招募对象参加访谈不随机，RDS可能会产生有偏估计。我们探究了在一项RDS研究中检测到的偏差是否归因于上述任何一种机制，并提出并应用权重来减少因非随机参加访谈而导致的偏差。
利用来自总体人群以及招募者向其发放优惠券的人群的数据，我们探究了年龄和社会经济地位与获得优惠券之间的关联，以及如果获得优惠券，与参加访谈之间的关联。通过按获得优惠券的假定逆概率加权（如同现有RDS方法那样），以及按年龄和社会经济地位组对如果获得优惠券则参加访谈的情况加权，来估计人群比例。
年轻男性招募不足主要是因为他们获得优惠券的可能性较小。社会经济地位较高的男性招募不足部分原因在于他们参加访谈的可能性较小。与这些发现一致，按年龄和社会经济地位组对非随机参加访谈进行加权，极大地改善了对社会经济地位最低组男性比例的估计，将RDS社会经济地位估计值的均方根误差降低了38%，但对年龄估计影响不大。加权还改善了对部落和宗教的估计（将均方根误差降低了19 - 29%），但对性活动或艾滋病毒感染状况估计影响不大。
从招募者处收集的关于他们向其发放优惠券的男性特征的数据，可用于减少RDS研究中的偏差。需要对这种新方法进行进一步评估。
