UK multiple sclerosis risk-sharing scheme: a new natural history dataset and an improved Markov model.

OBJECTIVES

In 2002, the UK's National Institute for Health and Care Excellence concluded that the multiple sclerosis (MS) disease modifying therapies; interferon-β and glatiramer acetate, were not cost effective over the short term but recognised that reducing disability over the longer term might dramatically improve the cost effectiveness. The UK Risk-sharing Scheme (RSS) was established to ensure cost-effective provision by prospectively collecting disability-related data from UK-treated patients with MS and comparing findings to a natural history (untreated) cohort. However, deficiencies were found in the originally selected untreated cohort and the resulting analytical approach. This study aims to identify a more suitable natural history cohort and to develop a robust analytical approach using the new cohort.

DESIGN

The Scientific Advisory Group, recommended the British Columbia Multiple Sclerosis (BCMS) database, Canada, as providing a more suitable natural history comparator cohort. Transition probabilities were derived and different Markov models (discrete and continuous) with and without baseline covariates were applied.

SETTING

MS clinics in Canada and the UK.

PARTICIPANTS

From the BCMS database, 898 'untreated' patients with MS considered eligible for drug treatment based on the UK's Association of British Neurologists criteria.

OUTCOME MEASURE

The predicted Expanded Disability Status Scale (EDSS) score was collected and assessed for goodness of fit when compared with actual outcome.

RESULTS

The BCMS untreated cohort contributed 7335 EDSS scores over a median 6.4 years (6357 EDSS 'transitions' recorded at consecutive visits) during the period 1980-1995. A continuous Markov model with 'onset age' as a binary covariate was deemed the most suitable model for future RSS analysis.

CONCLUSIONS

A new untreated MS cohort from British Columbia has been selected and will be modelled using a continuous Markov model with onset age as a baseline covariate. This approach will now be applied to the treated UK RSS MS cohort for future price adjustment calculations.