Bayesian transition models for ordinal longitudinal outcomes.
Stat Med
; 2024 Jun 09.
Article
em En
| MEDLINE
| ID: mdl-38853380
ABSTRACT
Ordinal longitudinal outcomes are becoming common in clinical research, particularly in the context of COVID-19 clinical trials. These outcomes are information-rich and can increase the statistical efficiency of a study when analyzed in a principled manner. We present Bayesian ordinal transition models as a flexible modeling framework to analyze ordinal longitudinal outcomes. We develop the theory from first principles and provide an application using data from the Adaptive COVID-19 Treatment Trial (ACTT-1) with code examples in R. We advocate that researchers use ordinal transition models to analyze ordinal longitudinal outcomes when appropriate alongside standard methods such as time-to-event modeling.
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1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Stat Med
Ano de publicação:
2024
Tipo de documento:
Article