Assessing the cost-effectiveness of new pharmaceuticals in epilepsy in adults: The results of a probabilistic decision model

Epilepsy currently affects more than 400,000 people in the United Kingdom and 2.3 million in the United States. Drug therapy is the mainstay of treatment for patients with epilepsy, but therapies vary widely in their mechanism of action and acquisition cost. This article describes a decision model developed for the National Institute for Clinical Excellence in the United Kingdom. It compares the long-term cost-effectiveness of drugs licensed in adults for use in 3 situations: monotherapy for newly diagnosed patients, monotherapy for refractory patients, and combination therapy for refractory patients. The analysis separately considers the treatment of partial and generalized seizures. The full range of pharmaceutical therapies feasibly used in the UK health system was included in the analysis. The analysis showed that, on the basis of existing evidence, for newly diagnosed patients with partial seizures, carbamazepine and valproate are likely to be the most cost-effective monotherapies. Curbamazepine is likely to be the most cost-effective 2nd-line monotherapy for refractory patients, and oxcarbazepine would probably be the most cost-effective adjunctive therapy for refractory patients if the willingness to pay for additional health benefits is greater than El 8, 000 per quality-adjusted life year (QALY). For patients with generalized seizures, valproate is most likely to be cost-effective for newly diagnosed patients. For refractory patients, adjunctive topiramate is more cost-effective than monotherapy alone if the willingness to pay for additional health benefits is greater than 35,000 pound per QALY There is, however, considerable uncertainty regarding these results. Some of the methodological features of the study will be of value in designing cost-effectiveness analyses of other therapies for chronic conditions. These include the methods used to deal with the absence of head-to-head trial data and the need to reflect time dependency in Markov transition probabilities.