Related Experiment Video
Updated: Aug 6, 2026
A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
Causal effects of multiple sclerosis therapies in left-truncated registry data
Dagmawi Chilot Haile1, Ibrahima Diouf2, Serkan Ozakbas3
1Clinical Outcomes Research Unit, Department of Medicine, The University of Melbourne, Melbourne, VIC, Australia; Neuroimmunology Centre, Department of Neurology, Royal Melbourne Hospital, Melbourne, VIC, Australia; Department of Physiology, School of Medicine, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
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.
