@article { ISI:000339808700005,
	title = {Marginal structural Cox models for estimating the association between beta-interferon exposure and disease progression in a multiple sclerosis cohort},
	journal = {American Journal of Epidemiology},
	volume = {180},
	number = {2},
	year = {2014},
	pages = {160-171},
	publisher = {OXFORD UNIV PRESS INC},
	address = {JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA},
	abstract = {<p>Longitudinal observational data are required to assess the association between exposure to beta-interferon medications and disease progression among relapsing-remitting multiple sclerosis (MS) patients in the {\textquoteleft}{\textquoteleft}real-world{\textquoteright}{\textquoteright} clinical practice setting. Marginal structural Cox models (MSCMs) can provide distinct advantages over traditional approaches by allowing adjustment for time-varying confounders such as MS relapses, as well as baseline characteristics, through the use of inverse probability weighting. We assessed the suitability of MSCMs to analyze data from a large cohort of 1,697 relapsing-remitting MS patients in British Columbia, Canada (1995-2008). In the context of this observational study, which spanned more than a decade and involved patients with a chronic yet fluctuating disease, the recently proposed {\textquoteleft}{\textquoteleft}normalized stabilized{\textquoteright}{\textquoteright} weights were found to be the most appropriate choice of weights. Using this model, no association between beta-interferon exposure and the hazard of disability progression was found (hazard ratio = 1.36, 95\% confidence interval: 0.95, 1.94). For sensitivity analyses, truncated normalized unstabilized weights were used in additional MSCMs and to construct inverse probability weight-adjusted survival curves; the findings did not change. Additionally, qualitatively similar conclusions from approximation approaches to the weighted Cox model (i.e., MSCM) extend confidence in the findings.</p>
},
	keywords = {bias (epidemiology), causality, confounding factors (epidemiology), epidemiologic methods, inverse probability weighting, marginal structural Cox model, multiple sclerosis, survival analysis},
	doi = {10.1093/aje/kwu125},
	author = {Karim, ME and Gustafson, P and Petkau, J and Zhao, Y and Shirani, A and Kingwell, E and Evans, C and van der Kop, M and Oger, J and Tremlett, H}
}
