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Estimation of Transition Probabilities from a Large Cohort (> 6000) of Australians Living with Multiple Sclerosis (MS) for Changing Disability Severity Classifications, MS Phenotype, and Disease-Modifying Therapy Classifications

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  • Julie A. Campbell

    (Menzies Institute for Medical Research, University of Tasmania)

  • Glen J. Henson

    (Menzies Institute for Medical Research, University of Tasmania)

  • Valery Fuh Ngwa

    (Menzies Institute for Medical Research, University of Tasmania)

  • Hasnat Ahmad

    (Menzies Institute for Medical Research, University of Tasmania)

  • Bruce V. Taylor

    (Menzies Institute for Medical Research, University of Tasmania)

  • Ingrid Mei

    (Menzies Institute for Medical Research, University of Tasmania)

  • Andrew J. Palmer

    (Menzies Institute for Medical Research, University of Tasmania)

Abstract

Background Multiple sclerosis (MS) is a chronic autoimmune/neurodegenerative disease associated with progressing disability affecting mostly women. We aim to estimate transition probabilities describing MS-related disability progression from no disability to severe disability. Transition probabilities are a vital input for health economics models. In MS, this is particularly relevant for pharmaceutical agency reimbursement decisions for disease-modifying therapies (DMTs). Methods Data were obtained from Australian participants of the MSBase registry. We used a four-state continuous-time Markov model to describe how people with MS transition between disability milestones defined by the Expanded Disability Status Scale (scale 0–10): no disability (EDSS of 0.0), mild (EDSS of 1.0–3.5), moderate (EDSS of 4.0–6.0), and severe (EDSS of 6.5–9.5). Model covariates included sex, DMT usage, MS-phenotype, and disease duration, and analysis of covariate groups were also conducted. All data were recorded by the treating neurologist. Results A total of N = 6369 participants (mean age 42.5 years, 75.00% female) with 38,837 person-years of follow-up and 54,570 clinical reviews were identified for the study. Annual transition probabilities included: remaining in the no, mild, moderate, and severe states (54.24%, 82.02%, 69.86%, 77.83% respectively) and transitioning from no to mild (42.31%), mild to moderate (11.38%), and moderate to severe (9.41%). Secondary-progressive MS was associated with a 150.9% increase in the hazard of disability progression versus relapsing–remitting MS. Conclusions People with MS have an approximately 45% probability of transitioning from the no disability state after one year, with people with progressive MS transitioning from this health state at a much higher rate. These transition probabilities will be applied in a publicly available health economics simulation model for Australia and similar populations, intended to support reimbursement of a plethora of existing and upcoming interventions including medications to reduce progression of MS.

Suggested Citation

  • Julie A. Campbell & Glen J. Henson & Valery Fuh Ngwa & Hasnat Ahmad & Bruce V. Taylor & Ingrid Mei & Andrew J. Palmer, 2025. "Estimation of Transition Probabilities from a Large Cohort (> 6000) of Australians Living with Multiple Sclerosis (MS) for Changing Disability Severity Classifications, MS Phenotype, and Disease-Modif," PharmacoEconomics, Springer, vol. 43(2), pages 223-239, February.
  • Handle: RePEc:spr:pharme:v:43:y:2025:i:2:d:10.1007_s40273-024-01417-4
    DOI: 10.1007/s40273-024-01417-4
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    References listed on IDEAS

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    1. Briggs, Andrew & Sculpher, Mark & Claxton, Karl, 2006. "Decision Modelling for Health Economic Evaluation," OUP Catalogue, Oxford University Press, number 9780198526629, Decembrie.
    2. Drummond, Michael F. & Sculpher, Mark J. & Claxton, Karl & Stoddart, Greg L. & Torrance, George W., 2015. "Methods for the Economic Evaluation of Health Care Programmes," OUP Catalogue, Oxford University Press, edition 4, number 9780199665884, Decembrie.
    3. Gray, Alastair M. & Clarke, Philip M. & Wolstenholme, Jane L. & Wordsworth, Sarah, 2010. "Applied Methods of Cost-effectiveness Analysis in Healthcare," OUP Catalogue, Oxford University Press, number 9780199227280, Decembrie.
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