title: A longitudinal model for disease progression was developed and applied to multiple sclerosis.
abstract: OBJECTIVE
  To develop a model of disease progression using multiple sclerosis (MS) as an exemplar.
  
  
  METHODS
  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 modeling was used to estimate the EDSS trajectory over time in one data set 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.
  
  
  RESULTS
  The best-fitting model for the UoWMS cohort (404 individuals, and 2,290 EDSS observations) included a nonlinear 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 and 7,335 EDSS observations) led to a model with similar time (years) coefficients, time [0.22 (95% confidence interval {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 BCMS and UoWMS, respectively.
  
  
  CONCLUSIONS
  It is possible to develop robust models of disability progression for chronic disease. However, explicit validation is important given the complex methodological challenges faced.
authors.affiliation: School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, BS8 2PS, UK. Electronic address: Michael.Lawton@bristol.ac.uk.
authors.forename: Michael
authors.initials: M
authors.lastname: Lawton
authors.affiliation: School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, BS8 2PS, UK.
authors.forename: Kate
authors.initials: K
authors.lastname: Tilling
authors.affiliation: Institute of Psychological Medicine and Clinical Neuroscience, Cardiff University, University Hospital of Wales, Cardiff, CF14 4XN, UK.
authors.forename: Neil
authors.initials: N
authors.lastname: Robertson
authors.affiliation: Faculty of Medicine (Neurology), UBC Hospital, 2211 Wesbrook Mall, University of British Columbia, Vancouver, BC V6T 2B5 Canada.
authors.forename: Helen
authors.initials: H
authors.lastname: Tremlett
authors.affiliation: Faculty of Medicine (Neurology), UBC Hospital, 2211 Wesbrook Mall, University of British Columbia, Vancouver, BC V6T 2B5 Canada.
authors.forename: Feng
authors.initials: F
authors.lastname: Zhu
authors.affiliation: Institute of Psychological Medicine and Clinical Neuroscience, Cardiff University, University Hospital of Wales, Cardiff, CF14 4XN, UK.
authors.forename: Katharine
authors.initials: K
authors.lastname: Harding
authors.affiliation: Faculty of Medicine (Neurology), UBC Hospital, 2211 Wesbrook Mall, University of British Columbia, Vancouver, BC V6T 2B5 Canada.
authors.forename: Joel
authors.initials: J
authors.lastname: Oger
authors.affiliation: School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, BS8 2PS, UK.
authors.forename: Yoav
authors.initials: Y
authors.lastname: Ben-Shlomo
mesh_terms: D000328:Adult; D018450:Disease Progression; D005260:Female; D006801:Humans; D008297:Male; D015233:Models, Statistical; D009103:Multiple Sclerosis / Q000453:epidemiology
keywords: Fractional polynomials; Multilevel model; Multiple sclerosis; Observational cohorts; Prognosis; Repeated measures model