Marginal Structural Cox Models for Estimating the Association Between beta-Interferon Exposure and Disease Progression in a Multiple Sclerosis Cohort

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Marginal Structural Cox Models for Estimating the Association Between beta-Interferon Exposure and Disease Progression in a Multiple Sclerosis Cohort

TitleMarginal Structural Cox Models for Estimating the Association Between beta-Interferon Exposure and Disease Progression in a Multiple Sclerosis Cohort
Publication TypeJournal Article
Year of Publication2014
AuthorsKarim, MEhsanul, Gustafson, P, Petkau, J, Zhao, Y, Shirani, A, Kingwell, E, Evans, C, van der Kop, M, Oger, J, Tremlett, H
JournalAMERICAN JOURNAL OF EPIDEMIOLOGY
Volume180
Pagination160-171
Date PublishedJUL 15
Type of ArticleArticle
ISSN0002-9262
Keywordsbias (epidemiology), causality, confounding factors (epidemiology), epidemiologic methods, inverse probability weighting, marginal structural Cox model, multiple sclerosis, survival analysis
AbstractLongitudinal 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.
DOI10.1093/aje/kwu125