Errors in reported degrees and respondent driven sampling: implications for bias.
Bottom Line: There is a substantial risk of bias in estimates from RDS if degrees are not correctly reported.RDS questionnaires should be refined to obtain high resolution degree information, particularly from low-degree individuals.Additionally, larger sample sizes can reduce uncertainty in estimates.
Affiliation: MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Dynamics, Imperial College London, St Mary's Hospital, Norfolk Place, London W2 1PG, UK. Electronic address: firstname.lastname@example.org.Show MeSH
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Mentions: While nearly all of the simulated consecutive samples correctly identified that prevalence increased between the two times points, there were marked differences in the estimates of the sampled trend (Fig. 3 and Table S5). The true simulated prevalence on average increased from 19% to 30% in the two year gap. The raw sample data overestimated prevalence, but identified the trend correctly. When the sample was adjusted using the Volz–Heckathorn estimator with accurate degrees the average difference across all 100 repeats was very close to the actual increase (3rd boxplot from the top in Fig. 3). However, the variation between individual paired samples was very large, indicating large inaccuracies in individual runs. As repeated samples are impractical in reality, this implies that conclusions from consecutive studies have a high probability of being quite inaccurate, even if degrees are correctly given. This is the case for all of the rounding methods we compared.
Affiliation: MRC Centre for Outbreak Analysis and Modelling, Department of Infectious Disease Dynamics, Imperial College London, St Mary's Hospital, Norfolk Place, London W2 1PG, UK. Electronic address: email@example.com.