Estimation and correction of bias in network simulations based on respondent-driven sampling data.

ABSTRACT: Respondent-driven sampling (RDS) is widely used for collecting data on hard-to-reach populations, including information about the structure of the networks connecting the individuals. Characterizing network features can be important for designing and evaluating health programs, particularly those that involve infectious disease transmission. While the validity of population proportions estimated from RDS-based datasets has been well studied, little is known about potential biases in inference about network structure from RDS. We developed a mathematical and statistical platform to simulate network structures with exponential random graph models, and to mimic the data generation mechanisms produced by RDS. We used this framework to characterize biases in three important network statistics -

SUBMITTER: Zhu L 

PROVIDER: S-EPMC7156755 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature