Causal effects of multiple sclerosis therapies in left-truncated registry data
Multiple Sclerosis Journal, 2026 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2026
- Doi Numarası: 10.1177/13524585261459943
- Dergi Adı: Multiple Sclerosis Journal
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, BIOSIS, EMBASE, MEDLINE, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
- Anahtar Kelimeler: causal inference, disease-modifying therapies, left-truncation, marginal structural modelling, Multiple sclerosis
- Ondokuz Mayıs Üniversitesi Adresli: Evet
Özet
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.