A longitudinal model for disease progression was developed and applied to multiple sclerosis

Michael Lawton*, Kate Tilling, Neil P Robertson, Helen Tremlett, Feng Zhu, Katharine E Harding, Joel Oger, Yoav Ben-Shlomo

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

11 Citations (Scopus)
248 Downloads (Pure)


To develop a model of disease progression using multiple sclerosis (MS) as an exemplar.

Study Design and Settings
Two observational cohorts, the University of Wales MS (UoWMS), UK (1976-) and British Columbia MS (BCMS) database, Canada (1980-) with longitudinal disability data (the Expanded Disability Status Scale, EDSS) were used; individuals potentially eligible for MS disease-modifying drugs treatments, but who were unexposed, were selected. Multilevel modelling was used to estimate the EDSS trajectory over time in one dataset and validated in the other; challenges addressed included the choice and function of time axis, complex observation-level variation, adjustments for MS relapses and autocorrelation.

The best-fitting model for the UoWMS cohort (404 individuals, 2290 EDSS observations) included a non-linear function of time since onset. Measurement error decreased over time and ad-hoc methods reduced autocorrelation and the effect of relapse. Replication within the BCMS cohort (978 individuals, 7335 EDSS observations) led to a model with similar time (years) coefficients (time (0.22 [95%CI:0.19-0.26], 0.16 [95%CI:0.10-0.22]) and log time (-0.13 [95%CI:-0.39-0.14], -0.15 [95%CI:-0.70-0.40]) for UoWMS and BCMS respectively).

It is possible to develop robust models of disability progression for chronic disease. However, explicit validation is important given the complex methodological challenges faced.
Original languageEnglish
Pages (from-to)1355-1365
Number of pages11
JournalJournal of Clinical Epidemiology
Issue number11
Early online date14 May 2015
Publication statusPublished - Nov 2015


  • Multiple Sclerosis
  • Repeated Measures Model
  • Multilevel Model
  • Fractional Polynomials
  • prognosis
  • observational cohorts


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