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Regression analysis of multivariate interval-censored failure time data with informative censoring.
Yu, Mengzhu; Feng, Yanqin; Duan, Ran; Sun, Jianguo.
Afiliação
  • Yu M; Center for Applied Statistical Research and College of Mathematics, 12510Jilin University, China.
  • Feng Y; School of Mathematics and Statistics, 12390Wuhan University, China.
  • Duan R; Alexion Pharmaceuticals, USA.
  • Sun J; Department of Statistics, 2628University of Missouri, USA.
Stat Methods Med Res ; 31(3): 391-403, 2022 03.
Article em En | MEDLINE | ID: mdl-34878352
Regression analysis of multivariate interval-censored failure time data has been discussed by many authors1-6. For most of the existing methods, however, one limitation is that they only apply to the situation where the censoring is non-informative or the failure time of interest is independent of the censoring mechanism. It is apparent that this may not be true sometimes and as pointed out by some authors, the analysis that does not take the dependent censoring into account could lead to biased or misleading results7,8. In this study, we consider regression analysis of multivariate interval-censored data arising from the additive hazards model and propose an estimating equation-based approach that allows for the informative censoring. The method can be easily implemented and the asymptotic properties of the proposed estimator of regression parameters are established. Also we perform a simulation study for the evaluation of the proposed method and it suggests that the method works well for practical situations. Finally, the proposed approach is applied to a set of real data.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Stat Methods Med Res Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Projetos de Pesquisa Tipo de estudo: Diagnostic_studies / Prognostic_studies Idioma: En Revista: Stat Methods Med Res Ano de publicação: 2022 Tipo de documento: Article