Document Type
Article
Publication Date
11-2019
Publication Title
Royal Statistical Society
Volume
68
Issue
5
Abstract
People who inject drugs are an important population to study in order to reduce transmission of blood-borne illnesses including HIV and Hepatitis. In this paper we estimate the HIV and Hepatitis C prevalence among people who inject drugs, as well as the proportion of people who inject drugs who are female in Mauritius. Respondent driven sampling (RDS), a widely adopted link-tracing sampling design used to collect samples from hard-to-reach human populations, was used to collect this sample. The random walk approximation underlying many common RDS estimators assumes that each social relation (edge) in the underlying social network has an equal probability of being traced in the collection of the sample. This assumption does not hold in practice. We show that certain RDS estimators are sensitive to the violation of this assumption. In order to address this limitation in current methodology, and the impact it may have on prevalence estimates, we present a new method for improving RDS prevalence estimators using estimated edge inclusion probabilities, and apply this to data from Mauritius.
First Page
1411
Last Page
1429
Recommended Citation
Ott, Miles Q.; Gile, Krista J.; Harrison, Matthew T.; Johnston, Lisa G.; and Hogan, Joseph W., "Reduced Bias for Respondent Driven Sampling: Accounting for Non-Uniform Edge Sampling Probabilities in People Who Inject Drugs in Mauritius" (2019). Statistical and Data Sciences: Faculty Publications, Smith College, Northampton, MA.
https://scholarworks.smith.edu/sds_facpubs/12
Digital Object Identifier (DOI)
doi.org/10.1111/rssc.12353
Rights
Licensed to Smith College and distributed CC-BY under the Smith College Faculty Open Access Policy.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Comments
Author’s submitted manuscript.