|Title||Marginal structural Cox models for estimating the association between beta-interferon exposure and disease progression in a multiple sclerosis cohort|
|Publication Type||Journal Article|
|Year of Publication||2014|
|Authors||Karim, ME, Gustafson, P, Petkau, J, Zhao, Y, Shirani, A, Kingwell, E, Evans, C, van der Kop, M, Oger, J, Tremlett, H|
|Journal||American Journal of Epidemiology|
|Keywords||bias (epidemiology), causality, confounding factors (epidemiology), epidemiologic methods, inverse probability weighting, marginal structural Cox model, multiple sclerosis, survival analysis|
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 ``real-world'' 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 ``normalized stabilized'' 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.