LSHTM Research Online

Daniel, RM; Cousens, SN; De Stavola, BL; Kenward, MG; Sterne, JA; (2013) Methods for dealing with time-dependent confounding. Statistics in medicine, 32 (9). pp. 1584-618. ISSN 0277-6715 DOI: 10.1002/sim.5686

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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. Copyright © 2012 John Wiley & Sons, Ltd.<br/>

Item Type:	Article
Faculty and Department:	Faculty of Epidemiology and Population Health > Dept of Infectious Disease Epidemiology Faculty of Epidemiology and Population Health > Dept of Medical Statistics
Research Centre:	Centre for Statistical Methodology Centre for Global Non-Communicable Diseases (NCDs)
PubMed ID:	23208861
Web of Science ID:	317587000012
URI:	http://researchonline.lshtm.ac.uk/id/eprint/491687

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