Selvaraj Vadivoo, Boopathi Kangusamy, Paranjape Ramesh, Mehendale Sanjay
National Institute of Epidemiology, Indian Council of Medical Research, TNHB, Ayapakkam, Chennai, India.
National AIDS Research Institute, Bhosari, Pune, India.
Indian J Med Res. 2016 Sep;144(3):447-459. doi: 10.4103/0971-5916.198665.
Respondent-driven sampling (RDS) is widely used to sample hidden populations and RDS data are analyzed using specially designed RDS analysis tool (RDSAT). RDSAT estimates parameters such as proportions. Analysis with RDSAT requires separate weight assignment for individual variables even in a single individual; hence, regression analysis is a problem. RDS-analyst is another advanced software that can perform three methods of estimates, namely, successive sampling method, RDS I and RDS II. All of these are in the process of refinement and need special skill to perform analysis. We propose a simple approach to analyze RDS data for comprehensive statistical analysis using any standard statistical software.
We proposed an approach (RDS-MOD - respondent driven sampling-modified) that determines a single normalized weight (similar to RDS II of Volz-Heckathorn) for each participant. This approach converts the RDS data into clustered data to account the pre-existing relationship between recruits and the recruiters. Further, Taylor's linearization method was proposed for calculating confidence intervals for the estimates. Generalized estimating equation approach was used for regression analysis and parameter estimates of different software were compared.
The parameter estimates such as proportions obtained by our approach were matched with those from currently available special software for RDS data.
INTERPRETATION & CONCLUSIONS: The proposed weight was comparable to different weights generated by RDSAT. The estimates were comparable to that by RDS II approach. RDS-MOD provided an efficient and easy-to-use method of estimation and regression accounting inter-individual recruits' dependence.
应答者驱动抽样（RDS）被广泛用于对隐性人群进行抽样，且RDS数据使用专门设计的RDS分析工具（RDSAT）进行分析。RDSAT估计诸如比例等参数。使用RDSAT进行分析即使对于单个个体的单个变量也需要单独进行权重分配；因此，回归分析存在问题。RDS分析软件是另一种先进软件，它可以执行三种估计方法，即连续抽样法、RDS I和RDS II。所有这些方法都在完善过程中，并且需要特殊技能来进行分析。我们提出一种简单的方法，使用任何标准统计软件来分析RDS数据以进行全面的统计分析。
我们提出了一种方法（RDS-MOD——应答者驱动抽样修正法），该方法为每个参与者确定一个单一的标准化权重（类似于Volz-Heckathorn的RDS II）。这种方法将RDS数据转换为聚类数据，以考虑招募者与被招募者之间预先存在的关系。此外，还提出了泰勒线性化方法来计算估计值的置信区间。使用广义估计方程方法进行回归分析，并比较不同软件的参数估计值。
我们的方法获得的诸如比例等参数估计值与当前用于RDS数据的专用软件获得的估计值相匹配。
所提出的权重与RDSAT生成的不同权重相当。估计值与RDS II方法的估计值相当。RDS-MOD提供了一种高效且易于使用的估计和回归方法，同时考虑了个体间招募者的依赖性。
