Unequal Edge Inclusion Probabilities in Link-Tracing Network Sampling With Implications for Respondent-Driven Sampling

Respondent-Driven Sampling (RDS) is a widely adopted linktracing sampling design used to draw valid statistical inference from samples of populations for which there is no available sampling frame. RDS estimators rely upon the assumption that each edge (representing a relationship between two individuals) in the underlying network has an equal probability of being sampled. We show that this assumption is violated in even the simplest cases, and that RDS estimators are sensitive to the violation of this assumption

Bayesian Peer Calibration with Application to Alcohol Use

Peers are often able to provide important additional information to supplement self-reported behavioral measures. The study motivating this work collected data on alcohol in a social network formed by college students living in a freshman dormitory. By using two imperfect sources of information (self-reported and peer-reported alcohol consumption), rather than solely self-reports or peer-reports, we are able to gain insight into alcohol consumption on both the population and the individual level, as well as information on the discrepancy of individual peer-reports. We develop a novel Bayesian comparative calibration model for continuous, count and binary outcomes that uses covariate information to characterize the joint distribution of both self and peer-reports on the network for estimating peer-reporting discrepancies in network surveys, and apply this to the data for fully Bayesian inference. We use this model to understand the effects of covariates on both drinking behavior and peer-reporting discrepancies

Fixed Choice Design and Augmented Fixed Choice Design for Network Data with Missing Observations

The statistical analysis of social networks is increasingly used to understand social processes and patterns. The association between social relationships and individual behaviors is of particular interest to sociologists, psychologists, and public health researchers. Several recent network studies make use of the fixed choice design (FCD), which induces missing edges in the network data. Because of the complex dependence structure inherent in networks, missing data can pose very difficult problems for valid statistical inference. In this article, we introduce novel methods for accounting for the FCD censoring and introduce a new survey design, which we call the augmented fixed choice design (AFCD). The AFCD adds considerable information to analyses without unduly burdening the survey respondent, resulting in improvements over the FCD, and other existing estimators. We demonstrate this new method through simulation studies and an analysis of alcohol use in a network of undergraduate students living in a residence hall

Reduced Bias for Respondent Driven Sampling: Accounting for Non-Uniform Edge Sampling Probabilities in People Who Inject Drugs in Mauritius

People who inject drugs are an important population to study in order to reduce transmission of blood-borne illnesses including HIV and Hepatitis. In this paper we estimate the HIV and Hepatitis C prevalence among people who inject drugs, as well as the proportion of people who inject drugs who are female in Mauritius. Respondent driven sampling (RDS), a widely adopted link-tracing sampling design used to collect samples from hard-to-reach human populations, was used to collect this sample. The random walk approximation underlying many common RDS estimators assumes that each social relation (edge) in the underlying social network has an equal probability of being traced in the collection of the sample. This assumption does not hold in practice. We show that certain RDS estimators are sensitive to the violation of this assumption. In order to address this limitation in current methodology, and the impact it may have on prevalence estimates, we present a new method for improving RDS prevalence estimators using estimated edge inclusion probabilities, and apply this to data from Mauritius

Healthy Nebraska: Advancing Human Health and Developing Healthy Communities

Healthy Nebraska: Advancing Human Health and Developing Healthy Communities Every day, the Institute of Agriculture and Natural Resources (IANR) is putting together a wickedly complex puzzle, in which each faculty member, researcher, Extension educator, student, staff member, partner and stakeholder is a vitally important piece. As the pieces come together, we see a picture of the world in which IANR is making a meaningful difference in sustainable food, fuel, feed, and fiber production

A Population-Based Surveillance Study on the Epidemiology of Hepatitis C in Estonia

Background and objective: The hepatitis C virus (HCV)-infected patients serve as a reservoir for transmission of the disease to others and are at risk of developing chronic hepatitis C, cirrhosis, and hepatocellular carcinoma. Although the epidemiological data of high rate HCV infection have been obtained in many countries, such data are insufficient in Estonia. Therefore, the aim of the study was to analyze country-specific data on HCV patients. Materials and methods: Data about age, gender, diagnosis, possible risk factors, coinfections, HCV genotypes, liver fibrosis stages and extrahepatic manifestations were collected from 518 patients. Results: The most common risk factors for hepatitis C were injection drug use and tattooing in the 30–39 and 40–49 year age groups, and blood transfusion in the 50–59 and 60–69 year age groups. The other risk factors established were profession-related factors and sexual contact. The prevailing viral genotype among the HCV infected patients was genotype 1 (69% of the patients) followed by genotype 3 (25%). Genotypes 1 and 3 correlated with blood transfusions before 1994, drug injections and tattooing. Conclusions: Our study provides the best representation of genotype distribution across Estonia. As a result of the study, valuable data has been collected on hepatitis C patients in Estonia