Methods for dealing with time-dependent confounding.
Stat Med
; 32(9): 1584-618, 2013 Apr 30.
Article
em En
| MEDLINE
| ID: mdl-23208861
ABSTRACT
Longitudinal studies, where data are repeatedly collected on subjects over a period, are common in medical research. When estimating the effect of a time-varying treatment or exposure on an outcome of interest measured at a later time, standard methods fail to give consistent estimators in the presence of time-varying confounders if those confounders are themselves affected by the treatment. Robins and colleagues have proposed several alternative methods that, provided certain assumptions hold, avoid the problems associated with standard approaches. They include the g-computation formula, inverse probability weighted estimation of marginal structural models and g-estimation of structural nested models. In this tutorial, we give a description of each of these methods, exploring the links and differences between them and the reasons for choosing one over the others in different settings.
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Interpretação Estatística de Dados
/
Estudos Longitudinais
/
Modelos Estatísticos
Tipo de estudo:
Observational_studies
/
Risk_factors_studies
Limite:
Humans
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article