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

Estimating the effect of disease-modifying treatment of MS in observational studies is impaired by bias from unmeasured confounders, in particular indication bias. To show how instrumental variables (IVs) reduce bias. All patients with relapsing onset of MS 1996-2010, identified by the nationwide Da...

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Vydáno v:Multiple sclerosis Ročník 30; číslo 1; s. 113
Hlavní autoři: Koch-Henriksen, Nils Iørgen, Thygesen, Lau Caspar, Sørensen, Per Soelberg, Magyari, Melinda
Médium: Journal Article
Jazyk:angličtina
Vydáno: England 01.01.2024
Témata:
instrumental variables
causal inference
reverse causality
Multiple sclerosis treatment
ISSN:1477-0970, 1477-0970
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Shrnutí:Estimating the effect of disease-modifying treatment of MS in observational studies is impaired by bias from unmeasured confounders, in particular indication bias. To show how instrumental variables (IVs) reduce bias. 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. 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]. 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.
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ISSN:1477-0970
1477-0970
DOI:10.1177/13524585231201423