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Bivariate pseudo-observations for recurrent event analysis with terminal events.
Furberg, Julie K; Andersen, Per K; Korn, Sofie; Overgaard, Morten; Ravn, Henrik.
Afiliação
  • Furberg JK; Biostatistics GLP-1 and CV 1, Novo Nordisk A/S, Vandtårnsvej 114, Søborg, Denmark. jukf@novonordisk.com.
  • Andersen PK; Section of Biostatistics, University of Copenhagen, Copenhagen, Denmark.
  • Korn S; Biostatistics 1, LEO Pharma A/S, Ballerup, Denmark.
  • Overgaard M; Research unit for Biostatistics, Department of Public Health, Aarhus University, Aarhus, Denmark.
  • Ravn H; Biostatistics GLP-1 and CV 1, Novo Nordisk A/S, Vandtårnsvej 114, Søborg, Denmark.
Lifetime Data Anal ; 29(2): 256-287, 2023 04.
Article em En | MEDLINE | ID: mdl-34739680
ABSTRACT
The analysis of recurrent events in the presence of terminal events requires special attention. Several approaches have been suggested for such analyses either using intensity models or marginal models. When analysing treatment effects on recurrent events in controlled trials, special attention should be paid to competing deaths and their impact on interpretation. This paper proposes a method that formulates a marginal model for recurrent events and terminal events simultaneously. Estimation is based on pseudo-observations for both the expected number of events and survival probabilities. Various relevant hypothesis tests in the framework are explored. Theoretical derivations and simulation studies are conducted to investigate the behaviour of the method. The method is applied to two real data examples. The bivariate marginal pseudo-observation model carries the strength of a two-dimensional modelling procedure and performs well in comparison with available models. Finally, an extension to a three-dimensional model, which decomposes the terminal event per death cause, is proposed and exemplified.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Estatísticos Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Modelos Estatísticos Limite: Humans Idioma: En Ano de publicação: 2023 Tipo de documento: Article