Journal
STATISTICS IN MEDICINE
Volume 32, Issue 9, Pages 1584-1618Publisher
WILEY
DOI: 10.1002/sim.5686
Keywords
time-dependent confounding; g-computation formula; inverse probability weighting; g-estimation; marginal structural model; structural nested model
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Funding
- Medical Research Council, U.K. [G0701024]
- MRC [G0701024, G0800800] Funding Source: UKRI
- Medical Research Council [G0800800, G0701024] Funding Source: researchfish
- National Institute for Health Research [NF-SI-0611-10168] Funding Source: researchfish
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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 (c) 2012 John Wiley & Sons, Ltd.
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