Methods for dealing with time-dependent confounding

• Published in: Statistics in medicine Vol. 32; no. 9; pp. 1584 - 1618
• Main Authors: Daniel, R.M., Cousens, S.N., De Stavola, B.L., Kenward, M. G., Sterne, J. A. C.
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 30.04.2013; Wiley Subscription Services, Inc
• Subjects: Data Interpretation, Statistical; Estimating techniques; g-computation formula; g-estimation; Humans; inverse probability weighting; Longitudinal Studies; marginal structural model; Medical research; Medical statistics; Medical treatment; Models, Statistical; Probability distribution; structural nested model; time-dependent confounding
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.5686

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.