Cost-utility analysis of Interferon Beta-1b in the treatment of different types of Multiple Sclerosis
Background Economic evaluation of treatments in multiple sclerosis (MS) presents a challenge. The disease affects a number of different body functions and leads to severe disability over time, without however a strong effect on mortality. At onset, the majority of patients will have relapsing-remitting disease (RRMS) and will then convert to secondary-progressive disease (SPMS) overtime. However, the course of the disease is unpredictable, and the conversion to SPMS can take place at different times since onset and at different levels of disability for different patients. Relapses appear to occur with the same frequency at all levels of disability, but will diminish over time. The effectiveness of treatments can be measured in different ways such as disease activity, the number and the severity of relapses or the progression of functional disability, regardless of the type of MS. However, improvements in outcome achieved over a short term may have an effect on the disease in the longer term, and effectiveness data from clinical trials must therefore be extrapolated to the longer term, using modelling techniques. This requires good epidemiological data on the natural course of the disease, where disease progression is expressed with the same measures as in the clinical trials. Also to perform economic evaluations, a global outcome measure is required to capture the impact of treatments on the disease and the most frequently used such measure is quality-adjusted life years (QALYs). However, for QALYs to be used in cost-effectiveness analysis of MS, they must be related to a measure of the disease and disease progression. The Expanded Disability Status Scale (EDSS) provides a good measure of the disease and has been widely used in epidemiological studies and clinical trials, in all types of MS. Lastly, detailed economic data that can be related to the different levels of disability (EDSS) are required. Objective We have earlier proposed a basic framework for cost-effectiveness modelling in MS, and the original model has been updated, as new data have become available. The current study proposes a further development of the modelling technique and estimates the cost-effectiveness of treatment with interferon b-1b (IFNB-1b) in a defined patient population with active disease, both RRMS and SPMS, from a societal perspective in Sweden. Methods The framework of the earlier Markov model is used, where states are defined according to EDSS. Transition probabilities for the first years in the model are calculated from clinical trial data, and for the extrapolation from a large epidemiological database on the natural history of MS. In view of the fact that the number of relapses at given levels of disability did not differ between patients with RRMS or SPMS in any of the three datasets used in this analysis, and that conversion from RRMS to SPMS did not occur at well defined levels of disability, we combined data from two large clinical trials in RRMS and SPMS. Patients were selected on whether or not they had active disease at enrolment, defined as an increase in the EDSS by at least 1 point (0.5 points for scores between EDSS 6 and 7) or at least 2 relapses in the preceding 2 years. This allows simulating treatment start at any stage and for any type of the disease and estimating long-term consequences within the same model. The combination of the two types of MS is further supported by the fact that it has been shown in 3 observational studies that costs and quality of life at given EDSS levels are not different for patients with different types of the disease. Transition probabilities between the Markov states are estimated for both the clinical trial and the natural history cohorts using an ordered probit model. Transitions thus depend on several factors, including what state a patient is in, whether or not she/he has a relapse, age, age at onset of the disease, time since onset of the disease, age at treatment start. The base case simulations use mean costs and mean utilities in each state from a large observational study in Sweden. However, the model allows calculating acceptability curves, i.e. the probability with which the cost effectiveness ratio of a treatment scenario is below given levels of willingness-to-pay for a QALY, using the entire distribution of costs and utilities at each EDSS level. Costs and benefits are discounted with 3%. Results The base case assumes treatment with IFNB-1b during 36 months, with no further effect when treatment is stopped, and includes both patients with active RRMS and SPMS. Sensitivity analysis is presented for treatment during 54 months. The annual cost of IFNB-1b treatment was 102 587 SEK plus 1600 SEK for special monitoring, and was adjusted for compliance in the clinical trial. In the base-case treatment adds 13 000 to costs over 10 years, and the cost per QALY gained is 71 400 SEK. When the time horizon is increased to 15-25 years, treatment dominates no treatment (higher utility and lower cost). With treatment during 54 months, the cost per QALY is 353 800 SEK, all costs included. When treatment is started early, the cost-effectiveness ratio is higher, e.g. 643 100 SEK in state 2, as patients in these states progress only very slowly. In the net benefit approach, there is a 80% probability that the treatment initiated in states 3 or 4 (EDSS 4.0-5.5) is cost-effective, if the willingness to pay for a QALY is 400 000 to 600 000 SEK. At that level of willingness to pay, the probability in state 2 is 45%. Conclusions With this new model, which combines active RRMS and SPMS, the effect of early treatment on the long-term outcome can be estimated for the first time using patient-level clinical data for RRMS and SPMS, as well as natural history data. The combination of the two types of MS into one model is supported by the finding that, at given levels of EDSS, there was no difference in the number of annual relapses in the three clinical datasets used, nor in the mean cost and mean utilities in the observational study. The model is more flexible than previous models, as it includes individual patient demographics and the entire distribution of costs and utilities in the different states. It thus represents a valuable tool to estimate the cost-effectiveness of treating different patient groups with IFNB-1b.
|Date of creation:||28 Jul 2001|
|Contact details of provider:|| Postal: The Economic Research Institute, Stockholm School of Economics, P.O. Box 6501, 113 83 Stockholm, Sweden|
Phone: +46-(0)8-736 90 00
Fax: +46-(0)8-31 01 57
Web page: http://www.hhs.se/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Kobelt, Gisela & Lindgren, Peter & Parkin, David & Francis, David A. & Johnson, Michael & Bates, David & Jönsson, Bengt, 2000. "Costs and Quality of Life in Multiple Sclerosis. A Cross-Sectional Observational Study in the UK," SSE/EFI Working Paper Series in Economics and Finance 398, Stockholm School of Economics.
When requesting a correction, please mention this item's handle: RePEc:hhs:hastef:0459. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Helena Lundin)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.