| Author | Sperandei, Sandro | |
| Author | Bastos, Leonardo Soares | |
| Author | Ribeiro-Alves, Marcelo | |
| Author | Bastos, Francisco Inácio Pinkusfeld Monteiro | |
| Access date | 2019-06-25T12:30:09Z | |
| Available date | 2019-06-25T12:30:09Z | |
| Document date | 2018 | |
| Citation | SPERANDEI, Sandro et al. Social Networks. Assessing respondent-driven sampling: a simulation study across different networks. Social Networks, v. 52, p. 48-55, 2018. | en_US |
| ISSN | 0378-8733 | |
| URI | https://www.arca.fiocruz.br/handle/icict/33648 | |
| Language | eng | en_US |
| Publisher | Elsevier | en_US |
| Rights | open access | |
| Title | Assessing respondent-driven sampling: a simulation study across different networks | en_US |
| Type | Article | |
| DOI | 10.1016/j.socnet.2017.05.004 | |
| Abstract | The purpose was to assess RDS estimators in populations simulated with diverse connectivity characteristics, incorporating the putative influence of misreported degrees and transmission processes. Four populations were simulated using different random graph models. Each population was “infected” using four different transmission processes. From each combination of population x transmission, one thousand samples were obtained using a RDS-like sampling strategy. Three estimators were used to predict the population-level prevalence of the “infection”. Several types of misreported degrees were simulated. Also, samples were generated using the standard random sampling method and the respective prevalence estimates, using the classical frequentist estimator. Estimation biases in relation to population parameters were assessed, as well as the variance. Variability was associated with the connectivity characteristics of each simulated population. Clustered populations yield greater variability and no RDS-based strategy could address the estimation biases. Misreporting degrees had modest effects, especially when RDS estimators were used. The best results for RDS-based samples were observed when the “infection” was randomly attributed, without any relation with the underlying network structure. | en_US |
| Affilliation | Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Programa de Biologia Computacional e Sistemas. Rio de Janeiro, RJ, Brasil. | |
| Affilliation | Fundação Oswaldo Cruz. Instituto Oswaldo Cruz. Programa de Biologia Computacional e Sistemas. Rio de Janeiro, RJ, Brasil. | |
| Affilliation | Fundação Oswaldo Cruz. Instituto Nacional de Infectologia Evandro Chagas. Rio de Janeiro, RJ, Brasil. | |
| Affilliation | Fundação Oswaldo Cruz. Instituto de Comunicação e Informação Científica e Tecnológica em Saúde. Rio de Janeiro, RJ, Brasil. | |
| Subject | Respondent-driven sampling | en_US |
| Subject | Hidden population | en_US |
| Subject | Hard-to-reach population | en_US |
| Subject | Simulation method | en_US |
| Subject | Random graph | en_US |
| Subject | Epidemiology | en_US |
| Embargo date | 2020-06-25 | |
