Modelling disease progression in relapsing–remitting onset multiple sclerosis using multilevel models applied to longitudinal data from two natural history cohorts and one treated cohort
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Background: The ability to better predict disease progression represents a major unmet need in multiple sclerosis (MS), and would help to inform therapeutic and management choices. Objectives: To develop multilevel models using longitudinal data on disease progression in patients with relapsing–remitting MS (RRMS) or secondary-progressive MS (SPMS); and to use these models to estimate the association of disease-modifying therapy (DMT) with progression. Design: Secondary analysis of three MS cohorts. Setting: Two natural history cohorts: University of Wales Multiple Sclerosis (UoWMS) cohort, UK, and British Columbia Multiple Sclerosis (BCMS) cohort, Canada. One observational DMT-treated cohort: UK MS risk-sharing scheme (RSS). Participants: The UoWMS database has > 2000 MS patients and the BCMS database (as of 2009) has > 5900 MS patients. All participants who had definite MS (RRMS/SPMS), who reached the criteria set out by the Association of British Neurologists (ABN) for eligibility for DMT [i.e. age ≥ 18 years, Expanded Disability Status Scale (EDSS) score of ≤ 6.5, occurrence of two or more relapses in the previous 2 years] and who had at least two repeated outcome measures were included: 404 patients for the UoWMS cohort and 978 patients for the BCMS cohort. Through the UK MS RSS scheme, 5583 DMT-treated patients were recruited, with the analysis sample being the 4137 who had RRMS and were eligible and treated at baseline, with at least one valid EDSS score post baseline. Main outcome measures: EDSS score observations post ABN eligibility. Methods: We used multilevel models in the development cohort (UoWMS) to develop a model for EDSS score with time since ABN eligibility, allowing for covariates and appropriate transformation of outcome and/or time. These methods were then applied to the BCMS cohort to obtain a ‘natural history’ model for changes in the EDSS score with time. We then used this natural history model to predict the trajectories of EDSS score in treated patients in the UK MS RSS database. Differences between the progression predicted by the natural history model and the progression observed at 6 years’ follow-up for the UK MS RSS cohort were used as indicators of the effectiveness of the DMTs. Previously developed utility scores were assigned to each EDSS score, and differences in utility also examined. Results: The model best fitting the UoWMS data showed a non-linear increase in EDSS score over time since ABN eligibility. This model fitted the BCMS cohort data well, with similar coefficients, and the BCMS model predicted EDSS score in UoWMS data with little evidence of bias. Using the natural history model predicts EDSS score in a treated cohort (UK MS RSS) higher than that observed [by 0.59 points (95% confidence interval 0.54 to 0.64 points)] at 6 years post treatment. Limitations: Only two natural history cohorts were compared, limiting generalisability. The comparison of a treated cohort with untreated cohorts is observational, thus limiting conclusions about causality. Conclusions: EDSS score progression in two natural history cohorts of MS patients showed a similar pattern. Progression in the natural history cohorts was slightly faster than EDSS score progression in the DMT-treated cohort, up to 6 years post treatment. Future work: Long-term follow-up of randomised controlled trials is needed to replicate these findings and examine duration of any treatment effect. Funding details: The National Institute for Health Research Health Technology Assessment programme.
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A recent study showing that clinical scores can be predictive across centres suggests that lack of heritability is not the issue.
“a recent study showing that clinical scores can be predictive across centres suggests that lack of heritability is not the issue.78”
From Genome‐wide association studies of multiple sclerosis (2018) · cites this paper as [78]
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- Authors
- Tilling, K., Lawton, M., Robertson, N., Tremlett, H., Zhu, F., Harding, K., Oger, J., & Ben-Shlomo, Y.
- Affiliations
- University of Bristol (GB)
- Cardiff University (GB)
- University of British Columbia (CA)
- Faculty of Medicine, Department of Medicine, Division of Neurology, University of British Columbia, Vancouver, BC, Canada
- School of Social and Community Medicine, Bristol University, Bristol, UK
- Corresponding author
- Tilling, Kate
- Venue
- Health Technology Assessment
- Published
- 2016-10
- Volume
- 20
- Issue
- 81
- Pages
- 1-48
- ISSN
- 1366-5278 (ISSN-L)
- 2046-4924
- PMC
- PMC5116581
- OpenAlex
- W2553805777
- Cited by
- 43
- References
- 50
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 19 · 2023: 2 · 2022: 3 · 2021: 3 · 2020: 4 · 2019: 3 · 2018: 5
- FWCI
- 2.42
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Locations
- Health Technology Assessment (publishedVersion, cc-by)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Explore Bristol Research (publishedVersion, other-oa)
- ORCA Online Research @Cardiff (Cardiff University) (acceptedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Bristol Research (University of Bristol) (submittedVersion, other-oa)
- Funders
- Health Technology Assessment programme (10/55/01)
- Topics
- Health Sciences › Medicine › Pathology and Forensic Medicine › Multiple Sclerosis Research Studies
- Social Sciences › Economics, Econometrics and Finance › Economics and Econometrics › Health Systems, Economic Evaluations, Quality of Life
- Health Sciences › Medicine › Oncology › Polyomavirus and related diseases
- Keywords
- relapsing-remitting multiple sclerosis, secondary progressive multiple sclerosis, disease progression, disability progression, Expanded Disability Status Scale, disease-modifying therapy, natural history of disease, multilevel models, risk-sharing schemes, treatment effectiveness, treatment eligibility criteria
- MeSH terms
- Adjuvants, Immunologic, Disability Evaluation, Humans, Longitudinal Studies, Reproducibility of Results, Models, Statistical, Disease Progression, Multiple Sclerosis, Chronic Progressive, Multiple Sclerosis, Relapsing-Remitting
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