Imputação múltipla de dados faltantes: exemplo de aplicação no Estudo Pró-Saúde

• Ano de defesa: 2013
• Autor(a) principal: Rangel, Thaís de Paulo
• Orientador(a): Faerstein, Eduardo
• Banca de defesa: Junger, Washington Leite , Struchiner, Claudio José , Nunes, Luciana Neves , Cruz, Oswaldo Gonçalves
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade do Estado do Rio de Janeiro
• Programa de Pós-Graduação: Programa de Pós-Graduação em Saúde Coletiva
• Departamento: Centro Biomédico::Instituto de Medicina Social
• País: BR
• Palavras-chave em Português: Dados faltantes; Imputação múltipla; Análise de sobrevivência; Tutorial
• Palavras-chave em Inglês: Missing data; Multiple imputation; Survival analysis; Tutorial
• Área do conhecimento CNPq: CNPQ::CIENCIAS DA SAUDE::SAUDE COLETIVA::EPIDEMIOLOGIA
• Link de acesso: http://www.bdtd.uerj.br/handle/1/4209

Missing data are a common problem in epidemiologic studies and depending on the way they occur, the resulting estimates may be biased. Literature shows several techniques to deal with this subject and multiple imputation has been receiving attention in the recent years. This dissertation presents the results of applying multiple imputation of missing data in the context of the Pro-Saude Study, a longitudinal study among civil servants at a university in Rio de Janeiro, Brazil. In the first paper, after simulation of missing data, the variable color/race of the female servants was imputed and analyzed through a previously established survival model, which had the self-reported history of uterine leiomyoma as the outcome. The process has been replicated a hundred times in order to determine the distribution of the coefficient and standard errors of the variable being imputed. Although the data presented were cross-sectionally collected (baseline data of the Pro-Saude Study, gathered in 1999 and 2001), the following of the servants were determined using self-reported information. In this scenario, the Cox proportional hazards model could be applied. In the situations created, imputation showed adequate results, including in the performance analyses. The technique had a satisfactory effectiveness when the missing mechanism was MAR (Missing At Random) and the percent of missing data was 10. Imputing the missing information and combining the estimates of the 10 resulting datasets produced a bias of 0,0011 to black women and 0,0015 to brown (mixed-race) women, what corroborates the efficiency of multiple imputation in this scenario. In the second paper, a tutorial was created to guide the application of multiple imputation in epidemiologic studies, which should facilitate the use of the technique by Brazilian researchers who are still not familiarized with the procedure. Basic steps and important decisions necessary to impute a dataset are presented and one of the scenarios of the first paper is used as an application example. All the analyses were performed at R statistical software, version 2.15 and the scripts are presented at the end of the text.