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Causal Effects of Multiple Sclerosis Therapies in Left-Truncated Registry Data Publisher Pubmed

Summary: Can delayed registry entry distort MS treatment estimates? A simulation found left-truncation biased relapse effects; statistical adjustments reduced the distortion. #MultipleSclerosis #Epidemiology

Diouf I ; Ozakbas S ; Horakova D ; Havrdova E K ; Patti F ; Eichau S ; Alroughani R ; Prat A ; Girard M ; Terzi M ; Yamout B ; Khoury S J ; Grammond P ; Blanco Y Show All Authors
Authors
  1. Diouf I
  2. Ozakbas S
  3. Horakova D
  4. Havrdova E K
  5. Patti F
  6. Eichau S
  7. Alroughani R
  8. Prat A
  9. Girard M
  10. Terzi M
  11. Yamout B
  12. Khoury S J
  13. Grammond P
  14. Blanco Y
  15. Shaygannejad V
  16. Foschi M
  17. Surcinelli A
  18. Neri S
  19. Weinstock Guttman B
  20. Prevost J
  21. Barnett M
  22. Van Der Walt A
  23. Butzkueven H
  24. Van Pesch V
  25. Soysal A
  26. Spitaleri D
  27. Cartechini E
  28. Maimone D
  29. Ampapa R
  30. Laureys G
  31. Ramo Tello C
  32. Di Gregorio M
  33. Lapointe E
  34. Slee M
  35. Karabudak R
  36. Garber J
  37. Altintas A
  38. Sanchez Menoyo J L
  39. Castillo Trivino T
  40. Habek M
  41. Al Asmi A
  42. Al Harbi T
  43. Csepany T
  44. Taylor B
  45. Shalaby N
  46. Moore F
  47. Massey J
  48. Hardy T A
  49. Gray O
  50. Decoo D
  51. Simu M
  52. Rozsa C
  53. Stuart E A
  54. Sharmin S
  55. Roos I
  56. Kalincik T

Source: Multiple Sclerosis Journal Published:2026


Abstract

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. © The Author(s), 2026. This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access pages (https://us.sagepub.com/en-us/nam/open-access-at-sage).