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The UK's National Institute for Health and Care Excellence (NICE)1 technology appraisal of disease-modifying therapies for multiple sclerosis stated, “on the balance of their clinical and cost effectiveness, neither beta interferon nor glatiramer acetate is recommended for the treatment of multiple sclerosis in the National Health Service (NHS) in England and Wales”. However, recognising that the drugs might prove cost-effective if assessed over longer than the phase 3 trials, which are conducted for 2–3 years, NICE invited the relevant authorities to consider a strategy to deliver the drugs within a £36 000/quality-adjusted life years (QALY) target, modelled over 20 years. Thus the UK risk-sharing scheme was born, tracking the progress over 10 years of a cohort of patients who started disease-modifying treatments between 2002 and 2005.2
The 6-year analysis has just been published.3 Two different models were used to predict the course of the untreated cohort: a continuous Markov model (PAREXEL)4 and a multilevel model (University of Bristol, UK).5i Both used data from historical, untreated patients in the British Columbia multiple sclerosis database, who would have qualified for treatment if available. A value for utility (quality of life compared with full health) was assigned to each Expanded Disability Status Scale (EDSS) grade. Having modelled the decline in utility expected, it was calculated that a cost-effective treatment would need to reduce this by 38%.
The primary analysis of the scheme shows that, on aggregate, the drugs perform beyond target at 6 years, with both models showing a 42% reduction in the predicted progression of utility. This primary analysis is restricted to the cohort of 4137 (of 5610) who had …
Competing interests None declared.
Provenance and peer review Commissioned; externally peer reviewed. This paper was reviewed by Colin Mumford, Edinburgh, UK, and Jamie Stankiewicz, Boston, USA.
↵i Both methods look to predict the behaviour of a cohort over time based on their entry data alone, using figures derived from a reference population followed longitudinally (in our case from the British Columbia multiple sclerosis (BCMS) data set). For full details see refs. 4 and 5.
Markov model: Markov modelling is based on the chance of an individual, at reassessment, staying in the same EDSS state or moving to a different one. Probabilities were calculated for each transition from the BCMS data set and then applied to the risk-sharing scheme baseline data. The result of the modelling is expressed as the predicted percentage of individuals in each EDSS state at any time point.
Multilevel model: projected curves of EDSS progression with time from an entry EDSS were derived from analysis of the BCMS database. Unlike the Markov model which considers each transition independent from what has gone before, multilevel modelling factors in the length already spent in each EDSS state. The model permits non-linear trajectories for individuals and allows for decreasing variability over time (reflecting the observed natural history of multiple sclerosis with slower progression through the higher grades).