This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 66
Type: Topic Contributed
Date/Time: Sunday, August 1, 2010 : 4:00 PM to 5:50 PM
Sponsor: Health Policy Statistics Section
Abstract - #308294
Title: Network Model-Assisted Prevalence Estimation from Respondent-Driven Sampling Data
Author(s): Krista J. Gile*+ and Mark S. Hancock
Companies: Nuffield College and University of California, Los Angeles
Address: New Road, Oxford, International, OX1 1NF, United Kingdom
Keywords: social network ; survey sampling ; hidden population ; epidemiology ; model-assisted ; network sampling

Respondent-Driven Sampling is type of link-tracing network sampling used to study hard-to-reach populations. Beginning with a convenience sample, each person sampled is given 2-3 uniquely identified coupons to distribute to other members of the target population, making them eligible for enrolment in the study. This is effective at collecting large diverse samples from many populations.

Current estimation relies on sampling weights estimated by treating the sampling process as a random walk on the underlying network of social relations. These estimates are based on strong assumptions allowing the data to be treated as a probability sample. In particular, existing estimators assume a with-replacement sample with an ideal initial sample. We introduce a new estimator based on fitting a social network model, and demonstrate its ability to correct for biases due to the initial sample.

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