Inference for respondent-driven sampling with misclassification

Abstract

Respondent-driven sampling (RDS) is a sampling method designed to study hard-to-reach human populations. Beginning with a convenience sample, each participant receives a small number of coupons, which they distribute to their contacts who become eligible. RDS participants are asked to report on their number of contacts in the target population. Also, a set of characteristics is observed for each participant. Current prevalence estimators assume that these attributes are measured accurately. However, ignoring misclassification may lead to biased estimates.

The main contribution of this paper is to discuss two approaches to correct for the bias introduced by the misclassification on nodal attributes for existing RDS estimators. The two approaches leverage misclassification rates assumed to be available from external validation studies. Most importantly, our analysis identifies circumstances for which the performance of the correction methods is impaired in the specific context of RDS. The two methods that are discussed are an analytical correction for estimators of the Hájek estimator style and the Simulation Extrapolation Misclassification (SIMEX MC) approach. Extended methodology to estimate the uncertainty of the corrected estimators is also presented. The performance of the proposed methods is assessed under varying levels of known or uncertain misclassification error across simulated social networks of varying features. Finally, the methods are used to estimate HIV prevalence among people who inject drugs (PWID) and men who have sex with men (MSM) in India.