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Methods for dealing with time‐dependent confounding
Author(s) -
Daniel R.M.,
Cousens S.N.,
De Stavola B.L.,
Kenward M. G.,
Sterne J. A. C.
Publication year - 2012
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.5686
Subject(s) - marginal structural model , confounding , estimator , inverse probability , econometrics , computer science , statistics , estimation , computation , outcome (game theory) , mathematics , algorithm , bayesian probability , mathematical economics , management , posterior probability , economics
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. Copyright © 2012 John Wiley & Sons, Ltd.