New estimates for network sampling
share
Network sampling is used around the world for surveys of vulnerable, hard-to-reach populations including people at risk for HIV, opioid misuse, and emerging epidemics. The sampling methods include tracing social links to add new people to the sample. Current estimates from these surveys are inaccurate, with large biases and mean squared errors and unreliable confidence intervals. New estimators are introduced here which eliminate almost all of the bias, have much lower mean squared error, and enable confidence intervals with good properties. The improvement is attained by avoiding unrealistic assumptions about the population network and the design, instead using the topology of the sample network data together with the sampling design actually used. In simulations using the real network of an at-risk population, the new estimates eliminate almost all the bias and have mean squared-errors that are 2 to 92 times lower than those of current estimators. The new estimators are effective with a wide variety of network designs including those with strongly restricted branching such as Respondent-Driven Sampling and freely branching designs such as Snowball Sampling.
READ FULL TEXT