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Causal effects of multiple sclerosis therapies in left-truncated registry data
Journal article   Open access   Peer reviewed

Causal effects of multiple sclerosis therapies in left-truncated registry data

Dagmawi Chilot Haile, Ibrahima Diouf, Serkan Ozakbas, Dana Horakova, Eva Kubala Havrdova, Francesco Patti, Sara Eichau, Raed Alroughani, Alessandra Lugaresi, Valentina Tomassini, …
Multiple sclerosis, Vol.32(10), pp.1052-1068
2026
PMID: 42479470
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Published (Version of Record) Open Access CC BY-NC V4.0

Abstract

Multiple sclerosis marginal structural modelling disease-modifying therapies left-truncation causal inference
Background Left-truncation is an unrecorded interval between multiple sclerosis (MS) onset and initial data in observational studies. This delay may bias estimates of disease-modifying therapy (DMT) effectiveness, especially when determined by patient or disease characteristics. Objectives To examine whether causal effect estimates of DMTs over the full disease course can be reliably derived from left-truncated registry data. Methods We analysed data from MSBase (144 centres, 41 countries) to assess the impact of left-truncation on causal treatment effect estimates. Cox marginal structural models (MSMs) estimated hazard ratios (HRs) for relapses, disability worsening and improvement, considering left-truncation at random and not-at-random. Fixed-time truncation and multivariable adjustment were applied to remediate bias. Results The study included 5588 patients tracked from true MS onset. The null model, without left-truncation, estimated the DMT effect on relapse risk (HR = 0.64; 95% confidence interval (CI) = 0.54–0.77). Left-truncation inflated this estimate. Shorter random truncation (1 year) produced greater bias (HR = 0.34), decreasing with longer durations (3-year HR = 0.48). Truncation not-at-random biased relapse estimates (HR = 0.37). Disability outcomes were less sensitive. Conclusion MSMs can reliably estimate DMT effectiveness in left-truncated MS registry data, although accuracy depends on truncation mechanism and duration. Both random and not-at-random truncation impact relapse estimates. Disability outcomes appear less sensitive. Fixed-time truncation and covariate adjustment mitigated bias.

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