Using instrumental variables to correct for bias in real-world cohort studies of the effects of disease-modifying treatment in MS

Mult Scler. 2024 Jan;30(1):113-120. doi: 10.1177/13524585231201423. Epub 2023 Oct 3.

Abstract

Background: Estimating the effect of disease-modifying treatment of MS in observational studies is impaired by bias from unmeasured confounders, in particular indication bias.

Objective: To show how instrumental variables (IVs) reduce bias.

Methods: All patients with relapsing onset of MS 1996-2010, identified by the nationwide Danish Multiple Sclerosis Registry, were followed from onset. Exposure was treatment index throughout the first 12 years from onset, defined as a cumulative function of months without and with medium- or high-efficacy treatment, and outcomes were hazard ratios (HRs) per unit treatment index for sustained Expanded Disability Scale Score (EDSS) 4 and 6 adjusted for age at onset and sex, without and with an IV. We used the onset cohort (1996-2000; 2001-2005; 2006-2010) as an IV because treatment index increased across the cohorts.

Results: We included 6014 patients. With conventional Cox regression, HRs for EDSS 4 and 6 were 1.15 [95% CI: 1.13-1.18] and 1.17 [1.13-1.20] per unit treatment index. Only with IVs, we confirmed a beneficial effect of treatment with HRs of 0.86 [0.81-0.91] and 0.82 [0.74-0.90].

Conclusion: The use of IVs eliminates indication bias and confirms that treatment is effective in delaying disability. IVs could, under some circumstances, be an alternative to marginal structural models.

Keywords: Multiple sclerosis treatment; causal inference; instrumental variables; reverse causality.

MeSH terms

  • Cohort Studies
  • Humans
  • Multiple Sclerosis* / drug therapy
  • Multiple Sclerosis* / epidemiology
  • Multiple Sclerosis, Relapsing-Remitting* / drug therapy
  • Multiple Sclerosis, Relapsing-Remitting* / epidemiology
  • Proportional Hazards Models
  • Registries
  • Treatment Outcome