Improving precision by adjusting for prognostic baseline variables in randomized trials with binary outcomes, without regression model assumptions.
Contemp Clin Trials
; 54: 18-24, 2017 03.
Article
em En
| MEDLINE
| ID: mdl-28064029
ABSTRACT
In randomized clinical trials with baseline variables that are prognostic for the primary outcome, there is potential to improve precision and reduce sample size by appropriately adjusting for these variables. A major challenge is that there are multiple statistical methods to adjust for baseline variables, but little guidance on which is best to use in a given context. The choice of method can have important consequences. For example, one commonly used method leads to uninterpretable estimates if there is any treatment effect heterogeneity, which would jeopardize the validity of trial conclusions. We give practical guidance on how to avoid this problem, while retaining the advantages of covariate adjustment. This can be achieved by using simple (but less well-known) standardization methods from the recent statistics literature. We discuss these methods and give software in R and Stata implementing them. A data example from a recent stroke trial is used to illustrate these methods.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Ensaios Clínicos Controlados Aleatórios como Assunto
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Estatística como Assunto
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Ativador de Plasminogênio Tecidual
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Acidente Vascular Cerebral
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Fibrinolíticos
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Hemorragia Cerebral Intraventricular
Tipo de estudo:
Clinical_trials
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Guideline
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Prognostic_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2017
Tipo de documento:
Article