Estimating Design Effect and Calculating Sample Size for Respondent-Driven Sampling Studies of Injection Drug Users in the United States

Respondent-driven sampling (RDS) has become increasingly popular for sampling hidden populations, including injecting drug users (IDU). However, RDS data are unique and require specialized analysis techniques, many of which remain underdeveloped. RDS sample size estimation requires knowing design effect (DE), which can only be calculated post hoc. Few studies have analyzed RDS DE using real world empirical data. We analyze estimated DE from 43 samples of IDU collected using a standardized protocol. We find the previous recommendation that sample size be at least doubled, consistent with DE = 2, underestimates true DE and recommend researchers use DE = 4 as an alternate estimate when calculating sample size. A formula for calculating sample size for RDS studies among IDU is presented. Researchers faced with limited resources may wish to accept slightly higher standard errors to keep sample size requirements low. Our results highlight dangers of ignoring sampling design in analysis

Molecular Phylogeny and Evolution of Parabasalia with Improved Taxon Sampling and New Protein Markers of Actin and Elongation Factor-1α

BACKGROUND: Inferring the evolutionary history of phylogenetically isolated, deep-branching groups of taxa-in particular determining the root-is often extraordinarily difficult because their close relatives are unavailable as suitable outgroups. One of these taxonomic groups is the phylum Parabasalia, which comprises morphologically diverse species of flagellated protists of ecological, medical, and evolutionary significance. Indeed, previous molecular phylogenetic analyses of members of this phylum have yielded conflicting and possibly erroneous inferences. Furthermore, many species of Parabasalia are symbionts in the gut of termites and cockroaches or parasites and therefore formidably difficult to cultivate, rendering available data insufficient. Increasing the numbers of examined taxa and informative characters (e.g., genes) is likely to produce more reliable inferences. PRINCIPAL FINDINGS: Actin and elongation factor-1α genes were identified newly from 22 species of termite-gut symbionts through careful manipulations and seven cultured species, which covered major lineages of Parabasalia. Their protein sequences were concatenated and analyzed with sequences of previously and newly identified glyceraldehyde-3-phosphate dehydrogenase and the small-subunit rRNA gene. This concatenated dataset provided more robust phylogenetic relationships among major groups of Parabasalia and a more plausible new root position than those previously reported. CONCLUSIONS/SIGNIFICANCE: We conclude that increasing the number of sampled taxa as well as the addition of new sequences greatly improves the accuracy and robustness of the phylogenetic inference. A morphologically simple cell is likely the ancient form in Parabasalia as opposed to a cell with elaborate flagellar and cytoskeletal structures, which was defined as most basal in previous inferences. Nevertheless, the evolution of Parabasalia is complex owing to several independent multiplication and simplification events in these structures. Therefore, systematics based solely on morphology does not reflect the evolutionary history of parabasalids

Does Respondent Driven Sampling Alter the Social Network Composition and Health-Seeking Behaviors of Illicit Drug Users Followed Prospectively?

Respondent driven sampling (RDS) was originally developed to sample and provide peer education to injection drug users at risk for HIV. Based on the premise that drug users' social networks were maintained through sharing rituals, this peer-driven approach to disseminate educational information and reduce risk behaviors capitalizes and expands upon the norms that sustain these relationships. Compared with traditional outreach interventions, peer-driven interventions produce greater reductions in HIV risk behaviors and adoption of safer behaviors over time, however, control and intervention groups are not similarly recruited. As peer-recruitment may alter risk networks and individual risk behaviors over time, such comparison studies are unable to isolate the effect of a peer-delivered intervention. This analysis examines whether RDS recruitment (without an intervention) is associated with changes in health-seeking behaviors and network composition over 6 months. New York City drug users (N = 618) were recruited using targeted street outreach (TSO) and RDS (2006–2009). 329 non-injectors (RDS = 237; TSO = 92) completed baseline and 6-month surveys ascertaining demographic, drug use, and network characteristics. Chi-square and t-tests compared RDS- and TSO-recruited participants on changes in HIV testing and drug treatment utilization and in the proportion of drug using, sex, incarcerated and social support networks over the follow-up period. The sample was 66% male, 24% Hispanic, 69% black, 62% homeless, and the median age was 35. At baseline, the median network size was 3, 86% used crack, 70% used cocaine, 40% used heroin, and in the past 6 months 72% were tested for HIV and 46% were enrolled in drug treatment. There were no significant differences by recruitment strategy with respect to changes in health-seeking behaviors or network composition over 6 months. These findings suggest no association between RDS recruitment and changes in network composition or HIV risk, which supports prior findings from prospective HIV behavioral surveillance and intervention studies

Methods for Inference from Respondent-Driven Sampling Data

Respondent-driven sampling is a commonly used method for sampling from hard-to-reach human populations connected by an underlying social network of relations. Beginning with a convenience sample, participants pass coupons to invite their contacts to join the sample. Although the method is often effective at attaining large and varied samples, its reliance on convenience samples, social network contacts, and participant decisions makes it subject to a large number of statistical concerns. This article reviews inferential methods available for data collected by respondent-driven sampling

Respondent Driven Sampling: Determinants of Recruitment and a Method to Improve Point Estimation

Introduction Respondent-driven sampling (RDS) is a variant of a link-tracing design intended for generating unbiased estimates of the composition of hidden populations that typically involves giving participants several coupons to recruit their peers into the study. RDS may generate biased estimates if coupons are distributed non-randomly or if potential recruits present for interview non-randomly. We explore if biases detected in an RDS study were due to either of these mechanisms, and propose and apply weights to reduce bias due to non-random presentation for interview. Methods Using data from the total population, and the population to whom recruiters offered their coupons, we explored how age and socioeconomic status were associated with being offered a coupon, and, if offered a coupon, with presenting for interview. Population proportions were estimated by weighting by the assumed inverse probabilities of being offered a coupon (as in existing RDS methods), and also of presentation for interview if offered a coupon by age and socioeconomic status group. Results Younger men were under-recruited primarily because they were less likely to be offered coupons. The under-recruitment of higher socioeconomic status men was due in part to them being less likely to present for interview. Consistent with these findings, weighting for non-random presentation for interview by age and socioeconomic status group greatly improved the estimate of the proportion of men in the lowest socioeconomic group, reducing the root-mean-squared error of RDS estimates of socioeconomic status by 38%, but had little effect on estimates for age. The weighting also improved estimates for tribe and religion (reducing root-mean-squared-errors by 19–29%), but had little effect for sexual activity or HIV status. Conclusions Data collected from recruiters on the characteristics of men to whom they offered coupons may be used to reduce bias in RDS studies. Further evaluation of this new method is required

If You Are Not Counted, You Don’t Count: Estimating the Number of African-American Men Who Have Sex with Men in San Francisco Using a Novel Bayesian Approach

African-American men who have sex with men (AA MSM) have been disproportionately infected with and affected by HIV and other STIs in San Francisco and the USA. The true scope and scale of the HIV epidemic in this population has not been quantified, in part because the size of this population remains unknown. We used the successive sampling population size estimation (SS-PSE) method, a new Bayesian approach to population size estimation that incorporates network size data routinely collected in respondent-driven sampling (RDS) studies, to estimate the number of AA MSM in San Francisco. This method was applied to data from a 2009 RDS study of AA MSM. An estimate from a separate study of local AA MSM was used to model the prior distribution of the population size. Two-hundred and fifty-six AA MSM were included in the RDS survey. The estimated population size was 4917 (95 % CI 1267–28,771), using a flat prior estimated 1882 (95 % CI 919–2463) as a lower acceptable bound, and a large prior estimated 6762 (95 % CI 1994–13,863) as an acceptable upper bound. Point estimates from the SS-PSE were consistent with estimates from multiplier methods using external data. The SS-PSE method is easily integrated into RDS studies and therefore provides a simple and appealing tool to rapidly produce estimates of the size of key populations otherwise difficult to reach and enumerate