Prevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertainty

Detalhes bibliográficos
Autor(a) principal: Moschen, Lucas Machado
Data de Publicação: 2021
Idioma: eng
Título da fonte: Repositório Institucional do FGV (FGV Repositório Digital)
Texto Completo: https://hdl.handle.net/10438/31844
Resumo: Trabalho de conclusão de curso
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spelling Moschen, Lucas MachadoEscolas::EMApCarvalho, Luiz Max Fagundes de2022-04-18T12:19:53Z2022-04-18T12:19:53Z2021-12https://hdl.handle.net/10438/31844Trabalho de conclusão de cursoHard-to-reach populations are difficult to access for researchers or refuse to enrol in public health surveys, making enumeration and sampling challenges. Respondent-driven sampling (RDS) is a chain-referral technique used to recruit individuals from hard-to-reach populations. The survey encourages the participants to recruit their peers, giving incentives to each recruitment and for participation. Since there is no enumeration of the subjects, RDS is a non-probabilistic sampling strategy. Moreover, the graphical structure of RDS suffers from missing data, and several assumptions about the recruitment process are necessary. After having the sampled individuals, understanding their characteristics is a focus in epidemiology, given that these are usually high-risk populations to some diseases. Therefore, estimating the disease prevalence, the proportion of infected individuals, and the dependence among other observed variables is a critical step for public decision making. Diagnostic tests for disease identification are subject to misclassification, and incorporating their accuracy corrects biases in the prevalence estimation problem. This work proposes the use of regression techniques for prevalence estimation in respondent-driven samples. We use conditionally autoregressive models to represent correlation among the individuals induced by recruitment. In modern statistics, understanding situations with unknown information and quantifying them plays a significant role. We use Bayesian inference for uncertainty quantification for our models. In the Bayesian paradigm, probability distributions for quantities of interest represent the belief about them. We discuss different prior specification approaches for the parameters and examine uncertainty about the graph structure using a graphical model of RDS. To perform sampling from the parameter distribution, we used the Hamiltonian Monte Carlo sampler. Diagnostics of this method helped to improve our model programming. Verification of the model through simulation and external datasets showed robust results, and we propose model extensions for the limitations of this work.Populações de difícil acesso são difíceis para pesquisadores se aproximarem ou se recusam a se inscrever em pesquisas de saúde pública, tornando a enumeração e amostragem desafios. O Respondent-driven sampling (RDS) é uma técnica de referência em cadeia usada para recrutar indivíduos de populações difíceis de alcançar. A pesquisa incentiva os participantes a recrutarem seus pares, dando incentivos a cada recrutamento e à participação. Como não há enumeração dos sujeitos, o RDS é uma estratégia de amostragem não probabilística. Além disso, a estrutura gráfica do RDS sofre com a falta de dados e várias suposições sobre o processo de recrutamento são necessárias. Após a obtenção dos indivíduos amostrados, o entendimento de suas características é foco da epidemiologia, visto que se trata de populações geralmente de alto risco para algumas doenças. Portanto, estimar a prevalência da doença, a proporção de indivíduos infectados e a dependência a outras variáveis observadas é uma etapa crítica para a tomada de decisão pública. Os testes de diagnóstico para identificação de doenças estão sujeitos a erros de classificação e incorporar suas acurácias corrige vieses no problema de estimação de prevalência. Este trabalho propõe o uso de técnicas de regressão para estimativa de prevalência em Respondent-driven sampling. Usamos modelos condicionalmente autorregressivos para representar a correlação entre os indivíduos induzida pelo recrutamento. Na estatística moderna, entender situações com informações desconhecidas e quantificá-las desempenha um papel significativo. Usamos inferência bayesiana para quantificação de incerteza para nossos modelos. No paradigma bayesiano, as distribuições de probabilidade para quantidades de interesse representam a crença sobre elas. Discutimos diferentes abordagens de especificação anterior para os parâmetros e examinamos a incerteza sobre a estrutura do grafo usando um modelo gráfico de RDS. Para realizar a amostragem da distribuição dos parâmetros, usamos o amostrador Hamiltonian Monte Carlo. Os diagnósticos deste método ajudaram a melhorar a programação do nosso modelo. A verificação do modelo por meio de simulação e conjuntos de dados externos mostrou resultados robustos, e propomos extensões do modelo para as limitações deste trabalho.engRespondent-driven samplingRegression analysisBayesian inferencePrevalence estimationMisclassificationSensitivitySpecificityAnálise de regressãoInferência bayesianaEstimação de prevalênciaClassificação erradaSensibilidadeEspecificidadeMatemáticaTeoria bayesiana de decisão estatísticaAmostragem (Estatística)Estudos transversaisAnálise de regressãoPrevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertaintyTCinfo:eu-repo/semantics/publishedVersionreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas (FGV)instacron:FGVinfo:eu-repo/semantics/openAccessLICENSElicense.txtlicense.txttext/plain; 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dc.title.por.fl_str_mv Prevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertainty
title Prevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertainty
spellingShingle Prevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertainty
Moschen, Lucas Machado
Respondent-driven sampling
Regression analysis
Bayesian inference
Prevalence estimation
Misclassification
Sensitivity
Specificity
Análise de regressão
Inferência bayesiana
Estimação de prevalência
Classificação errada
Sensibilidade
Especificidade
Matemática
Teoria bayesiana de decisão estatística
Amostragem (Estatística)
Estudos transversais
Análise de regressão
title_short Prevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertainty
title_full Prevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertainty
title_fullStr Prevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertainty
title_full_unstemmed Prevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertainty
title_sort Prevalence estimation and binary regression methods for respondent-driven sampling with outcome uncertainty
author Moschen, Lucas Machado
author_facet Moschen, Lucas Machado
author_role author
dc.contributor.unidadefgv.por.fl_str_mv Escolas::EMAp
dc.contributor.author.fl_str_mv Moschen, Lucas Machado
dc.contributor.advisor1.fl_str_mv Carvalho, Luiz Max Fagundes de
contributor_str_mv Carvalho, Luiz Max Fagundes de
dc.subject.por.fl_str_mv Respondent-driven sampling
Regression analysis
Bayesian inference
Prevalence estimation
Misclassification
Sensitivity
Specificity
Análise de regressão
Inferência bayesiana
Estimação de prevalência
Classificação errada
Sensibilidade
Especificidade
topic Respondent-driven sampling
Regression analysis
Bayesian inference
Prevalence estimation
Misclassification
Sensitivity
Specificity
Análise de regressão
Inferência bayesiana
Estimação de prevalência
Classificação errada
Sensibilidade
Especificidade
Matemática
Teoria bayesiana de decisão estatística
Amostragem (Estatística)
Estudos transversais
Análise de regressão
dc.subject.area.por.fl_str_mv Matemática
dc.subject.bibliodata.por.fl_str_mv Teoria bayesiana de decisão estatística
Amostragem (Estatística)
Estudos transversais
Análise de regressão
description Trabalho de conclusão de curso
publishDate 2021
dc.date.issued.fl_str_mv 2021-12
dc.date.accessioned.fl_str_mv 2022-04-18T12:19:53Z
dc.date.available.fl_str_mv 2022-04-18T12:19:53Z
dc.type.driver.fl_str_mv TC
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
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dc.identifier.uri.fl_str_mv https://hdl.handle.net/10438/31844
url https://hdl.handle.net/10438/31844
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
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