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Errors in reported degrees and respondent driven sampling: implications for bias.

Mills HL, Johnson S, Hickman M, Jones NS, Colijn C - Drug Alcohol Depend (2014)

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.

View Article: PubMed Central - PubMed

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: harriet.l.mills@gmail.com.

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Related in: MedlinePlus

Boxplots indicating the prevalence estimates from simulated RDS surveys of 100 populations on networks with long-tailed degree distributions. The adjusted prevalence estimates (black) are always better than the raw, unadjusted data (grey), but incorrect degrees can cause significant inaccuracies in the estimated prevalence. The true prevalence in the simulated populations is indicated by the dashed line.
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fig0010: Boxplots indicating the prevalence estimates from simulated RDS surveys of 100 populations on networks with long-tailed degree distributions. The adjusted prevalence estimates (black) are always better than the raw, unadjusted data (grey), but incorrect degrees can cause significant inaccuracies in the estimated prevalence. The true prevalence in the simulated populations is indicated by the dashed line.

Mentions: Inaccuracy in reported degrees had a large effect on the reliability of estimates of prevalence and incidence (Fig. 2). The top half of Fig. 2 shows estimates of prevalence from RDS surveys where degree was mis-reported by the 5 rounding schemes. The estimates were calculated using the Volz–Heckathorn estimator. Mis-reporting degrees caused all surveys to over-estimate prevalence (compare to the ‘Actual’ prevalence in the whole network, top). However, if degrees were correctly reported (standard RDS) the average prevalence estimate from 100 surveys was accurate, but individual variation was large. Even with inaccurate degreees, the adjusted estimates (blue bars) were still closer to the true prevalence or incidence than the point estimate from the raw data (green bars).


Errors in reported degrees and respondent driven sampling: implications for bias.

Mills HL, Johnson S, Hickman M, Jones NS, Colijn C - Drug Alcohol Depend (2014)

Boxplots indicating the prevalence estimates from simulated RDS surveys of 100 populations on networks with long-tailed degree distributions. The adjusted prevalence estimates (black) are always better than the raw, unadjusted data (grey), but incorrect degrees can cause significant inaccuracies in the estimated prevalence. The true prevalence in the simulated populations is indicated by the dashed line.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4150985&req=5

fig0010: Boxplots indicating the prevalence estimates from simulated RDS surveys of 100 populations on networks with long-tailed degree distributions. The adjusted prevalence estimates (black) are always better than the raw, unadjusted data (grey), but incorrect degrees can cause significant inaccuracies in the estimated prevalence. The true prevalence in the simulated populations is indicated by the dashed line.
Mentions: Inaccuracy in reported degrees had a large effect on the reliability of estimates of prevalence and incidence (Fig. 2). The top half of Fig. 2 shows estimates of prevalence from RDS surveys where degree was mis-reported by the 5 rounding schemes. The estimates were calculated using the Volz–Heckathorn estimator. Mis-reporting degrees caused all surveys to over-estimate prevalence (compare to the ‘Actual’ prevalence in the whole network, top). However, if degrees were correctly reported (standard RDS) the average prevalence estimate from 100 surveys was accurate, but individual variation was large. Even with inaccurate degreees, the adjusted estimates (blue bars) were still closer to the true prevalence or incidence than the point estimate from the raw data (green bars).

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.

View Article: PubMed Central - PubMed

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: harriet.l.mills@gmail.com.

Show MeSH
Related in: MedlinePlus