Design-adherent estimators for network surveys

TL;DR

This paper introduces a new resampling-based estimation method for network surveys that improves accuracy and reduces bias in population estimates of key at-risk groups, especially in complex link-tracing designs.

Contribution

A novel resampling approach that estimates inclusion probabilities in network surveys, enhancing the accuracy of population estimates in complex sampling designs.

Findings

Increased accuracy of population estimates in simulations.

Significant reduction in bias compared to traditional methods.

Effective in complex link-tracing network surveys.

Abstract

Network surveys of key populations at risk for HIV are an essential part of the effort to understand how the epidemic spreads and how it can be prevented. Estimation of population values from the sample data has been probematical, however, because the link-tracing of the network surveys includes different people in the sample with unequal probabilities, and these inclusion probabilities have to be estimated accurately to avoid large biases in survey estimates. A new approach to estimation is introduced here, based on resampling the sample network many times using a design that adheres to main features of the design used in the field. These features include network link tracing, branching, and without-replacement sampling. The frequency that a person is included in the resamples is used to estimate the inclusion probability for each person in the original sample, and these estimates of inclusion probabilities are used in an unequal-probability estimator. In simulations using a population of drug users, sex workers, and their partners for which the actual values of population characteristics are known, the design-adherent estimation approach increases the accuracy of estimates of population quantities, largely by eliminating most of the biases.