Marginal structural cox models for estimating the association between β-interferon exposure and disease progression in a multiple sclerosis cohort

Mohammad Ehsanul Karim, Paul Gustafson, John Petkau, Yinshan Zhao, Afsaneh Shirani, Elaine Kingwell, Charity Evans, Mia Van Der Kop, Joel Oger, Helen Tremlett

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Longitudinal observational data are required to assess the association between exposure to β-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 β-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.

Original languageEnglish (US)
Pages (from-to)160-171
Number of pages12
JournalAmerican Journal of Epidemiology
Volume180
Issue number2
DOIs
StatePublished - Jul 15 2014
Externally publishedYes

Keywords

  • bias (epidemiology)
  • causality
  • confounding factors (epidemiology)
  • epidemiologic methods
  • inverse probability weighting
  • marginal structural Cox model
  • multiple sclerosis
  • survival analysis

ASJC Scopus subject areas

  • Epidemiology

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