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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

Degree estimates from two RDS surveys of PWIDs in Bristol, UK [20, 31], the question was ‘In the last 30 days how many people who inject drugs have you spoken to who you know by name and who also know you by name?’. (A) The full distributions. (B) Just degrees less than 100. The 2006 and 2009 distributions were very similar: the mean degree in both years was 39, with a standard deviation of 66.5 in 2006 and 60.0 in 2009.
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fig0005: Degree estimates from two RDS surveys of PWIDs in Bristol, UK [20, 31], the question was ‘In the last 30 days how many people who inject drugs have you spoken to who you know by name and who also know you by name?’. (A) The full distributions. (B) Just degrees less than 100. The 2006 and 2009 distributions were very similar: the mean degree in both years was 39, with a standard deviation of 66.5 in 2006 and 60.0 in 2009.

Mentions: We determine the impact of inaccurate degrees on the prevalence estimate by re-computing the estimate in Eq. (1) using , where are the individuals’ correct degrees in the network, and Δdi correspond to inaccuracies in these degrees. We consider five different rounding schemes to mimic patterns seen in data: (1) round all degrees up to the nearest 5, (2) round all degrees up to the nearest 10, (3) increase every degree by 5, and finally two methods to directly mimic patterns seen in the Bristol data (Fig. 1). These are (4) round all degrees between 10 and 100 to the nearest 10, and degrees greater than 100 to the nearest 100; and (5) similar, but individuals with degrees less than 10 are given a different degree between 1 and 10, chosen according to the distribution seen in the Bristol data.


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)

Degree estimates from two RDS surveys of PWIDs in Bristol, UK [20, 31], the question was ‘In the last 30 days how many people who inject drugs have you spoken to who you know by name and who also know you by name?’. (A) The full distributions. (B) Just degrees less than 100. The 2006 and 2009 distributions were very similar: the mean degree in both years was 39, with a standard deviation of 66.5 in 2006 and 60.0 in 2009.
© Copyright Policy - CC BY-NC-ND
Related In: Results  -  Collection

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

fig0005: Degree estimates from two RDS surveys of PWIDs in Bristol, UK [20, 31], the question was ‘In the last 30 days how many people who inject drugs have you spoken to who you know by name and who also know you by name?’. (A) The full distributions. (B) Just degrees less than 100. The 2006 and 2009 distributions were very similar: the mean degree in both years was 39, with a standard deviation of 66.5 in 2006 and 60.0 in 2009.
Mentions: We determine the impact of inaccurate degrees on the prevalence estimate by re-computing the estimate in Eq. (1) using , where are the individuals’ correct degrees in the network, and Δdi correspond to inaccuracies in these degrees. We consider five different rounding schemes to mimic patterns seen in data: (1) round all degrees up to the nearest 5, (2) round all degrees up to the nearest 10, (3) increase every degree by 5, and finally two methods to directly mimic patterns seen in the Bristol data (Fig. 1). These are (4) round all degrees between 10 and 100 to the nearest 10, and degrees greater than 100 to the nearest 100; and (5) similar, but individuals with degrees less than 10 are given a different degree between 1 and 10, chosen according to the distribution seen in the Bristol data.

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