Economic Evaluation of AEDs used as monotherapy in the treatment of adults with newly diagnosed focal epilepsy

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1 0 0 APPENDIX P Cost-effectiveness analyses Five economic models were developed as part of the guideline development, one for each of the following clinical areas:. Monotherapy for adults with newly diagnosed focal epilepsy. Adjunctive therapy for adults with refractory focal epilepsy. Monotherapy for children with newly diagnosed focal epilepsy. Adjunctive therapy for children with refractory focal epilepsy. Adjunctive therapy for adults with refractory generalised tonic-clonic seizures Each model is presented separately in the following sections, with the exception of the two models developed for the evaluation of AEDs used to treat children with focal epilepsy which are presented together. Where the same data or methods were used across different analyses, cross references have been provided. 0 0 Economic Evaluation of AEDs used as monotherapy in the treatment of adults with newly diagnosed focal epilepsy Introduction The review of direct clinical evidence for AEDs used as monotherapy in adults with newly diagnosed focal epilepsy showed broad similarity between different drug options. A network meta-analysis was undertaken to simultaneously compare virtually all the AEDs assessed in the direct comparative data. Relative treatment effects estimated in the network meta-analysis showed broad similarity between different drug options, but results also showed sodium valproate to be the most effective drug in one-third of Monte Carlo simulations. Health economic evidence for AEDs as monotherapy in adults with newly diagnosed focal epilepsy was limited to two UK studies: one economic evaluation conducted alongside a recent UK randomised controlled trial (SANAD) and one model based cost-utility analysis undertaken to inform previous NICE guidance (TA). Both studies were well conducted, but each had limitations when considered within the current decision context. The model developed by Hawkins and colleagues estimated treatment effects from a network metaanalysis of clinical trials that met the inclusion criteria of their systematic review. However, many of the studies that met their inclusion criteria did not meet the inclusion criteria of the systematic review conducted for this guideline, predominantly because the level of population contamination (i.e. mixture of patients with focal and generalised epilepsy types) was greater than the threshold set out in the review protocol. A certain level of Page of

2 contamination can be expected in many studies of newly diagnosed epilepsy patients as diagnosis and classification can take time, but the guideline systematic review had a lower threshold than the one set out by Wilby and colleagues. Hawkins and colleagues also used different discounting rates than those currently outlined in the NICE reference case. The study by Marson and colleagues, SANAD, was an economic evaluation conducted alongside a pragmatic clinical trial with a -year follow-up. Two years is a very short time horizon on which to base decisions that may apply to a patient s lifetime, but the trial had a particular strength that should not be discounted. During the trial, treating clinicians were able to adjust dosage incrementally, as would occur in clinical practice. Thus the economic evaluation captured resource use, both in terms of drug dosage and clinician time, and effects over a longer period than some of the model-based evaluations using short-term, fixed-dose trial data. In addition to those highlighted above, both trials had two potentially serious limitations when considered in the current decision-making context. First, both evaluations used drug costs that were current at the time they were undertaken, in 00-0 for Hawkins and colleagues and 00 for Marson and colleagues. Since then, the unit costs for lamotrigine and topiramate have reduced dramatically as they came off patent. This change in cost could substantially alter the conclusions of the two studies and is therefore considered a potentially serious limitation. Second, evidence is now available for two additional drugs licensed for monotherapy in this group of patients: gabapentin and levetiracetam. As there was neither a license nor evidence to inform a comparison of these drugs, Hawkins and colleagues did not include either drug in their evaluation and Marson and colleagues did not include levetiracetam in theirs. The lack of cost-effectiveness data on these particular drugs of interest limits the applicability of the studies to the current decision-making context. Because of these various limitations, guideline recommendations for AEDs used as monotherapy in adults with newly diagnosed focal epilepsy could not be based on published economic evidence alone and original modelling was undertaken. The decision modelling presented here was developed in close collaboration between the health economist, NCGC technical team and GDG members. Methods.0. Model overview The analysis set out to evaluate the comparative cost-effectiveness of different AEDs used as monotherapy in the treatment of adults with newly diagnosed focal epilepsy. A costutility analysis was undertaken in line with the methods of the NICE reference case. QALYs were calculated using utility weights from EQ-D responses and UK public valuations. Costs were considered from a UK National Health Service and Personal Social Services perspective and expressed in 00-0 UK sterling. Healthcare costs associated with starting and switching therapies, additional healthcare costs required for the treatment of seizure free and not seizure free epilepsy patients and anti-epileptic drug costs were all included in the model. Costs of dealing with adverse events arising from treatment were not considered, as most will resolve when dose is adjusted or treatment is withdrawn. Page of

3 0 0 0 The cost-effectiveness analysis must be relevant for decision-making over the longer term, as most people with epilepsy can be expected to take AEDs for much of their lives. Therefore, the longer term costs and outcomes of treatment needed to be considered, including those associated with patients who do not respond to treatment. A year time horizon was considered sufficiently long enough to capture all relevant costs and benefits associated with competing drug treatments. In the basecase, the hypothetical cohort of patients was assumed to be 0 years old, but this was tested in a sensitivity analysis. To enable direct comparisons of treatments to be made based on the individual clinical trial results, a random effects network meta-analysis using a hierarchical Bayesian model was performed. Full details of this analysis are given in Appendix O. The clinical trial data provided response and withdrawal estimates for AEDs in the short term, typically up to months. Open-label clinical trial data was used to estimate the longer-term rates of drug effect and withdrawal. The model also accounted for epilepsy related mortality linked to whether patients were seizure-free or not seizure-free. In order to scale future costs and health benefits to their present value, costs and benefits were discounted at a rate of.% per annum i. The performance of alternative treatment sequences was estimated using incremental cost-effectiveness ratios (ICERs), defined as the added cost of a given strategy divided by its added benefit compared with the next most expensive strategy. A threshold of 0,000 per QALY gained was used to assess costeffectiveness. A probabilistic sensitivity analysis was undertaken to test the robustness of the results against the imprecision and uncertainty around input parameter point estimates (i.e. mean/median odds ratios, utility weights, etc). A probability distribution was defined for various model inputs and when the model is run, a value for each input was randomly selected from its specific probability distribution simultaneously and costs and QALYs were calculated using these random values. The model is run repeatedly in this case 0,000 times and results are summarised as mean costs and mean QALYs. Probability distributions in the analysis were based on error estimates from data sources, such as confidence intervals Model structure The structure of the decision-analytic model developed for this analysis was adapted from the model developed by Hawkins and colleagues. It was validated by the GDG as a reasonable reflection of current practice. It assumes that all hypothetical patients entering the model are newly diagnosed, treatment-naive individuals with focal epilepsy. All hypothetical patients start monotherapy and experience one of three outcomes: achieve seizure freedom; do not achieve seizure freedom (no response); or withdraw due to adverse i Discounting is a technique used to reflect the present value of a cost or a health benefit that will occur at some future date. Because there is an opportunity cost to spending money now and there is a desire to experience health benefits now rather than in the future, discounting gives future costs and health benefits less weight compared to present costs and benefits. Page of

4 0 0 events. Patients who achieve seizure freedom are assumed to continue on the drug treatment for subsequent cycles. Patients who do not achieve seizure freedom (nonresponders and those withdrawing due to adverse events) are assumed to move on to adjunctive therapy. Patients who start adjunctive therapy experience one of four outcomes: achieve seizure freedom; experience a reduction in seizure frequency of between 0% and %; experience a reduction of less than 0% (no response); or withdraw due to adverse events. Again, patients who respond either as seizure free or a 0% to % reduction in seizure frequency are assumed to continue adjunctive drug treatment for subsequent cycles. At this point, patients who are still refractory (non-responders and those withdrawing due to adverse events) are assumed to be maintained on monotherapy with an older AED. This was a simplification of reality, as these patients would normally be considered for further therapy or surgery. A multistate Markov model was created using TreeAge Pro 00 to capture the different costs and effects associated with a given drug treatment. It was built to reflect transitions between a set of mutually exclusive health states, defined by the aforementioned outcomes of treatment. The Markov model and how patients move through the pathway is illustrated in figure. Figure : Markov model of AED treatment 0 Page of

5 The consequences of a given drug treatment are reflected as a set of possible transitions between health states over a series of discrete time periods, called cycles. In figure, health states are depicted as ovals and interventions are depicted as rectangles. Movement between the various health states is governed by transition probabilities which are derived from the systematic review of clinical effectiveness data and from observational and openlabel clinical trial data. Thirty -month cycles were modelled, resulting in a -year time horizon for the analysis. The probability of a patient responding to a given drug and remaining on treatment after the first cycle was derived from the drug-specific clinical trial data synthesised in the network meta-analysis (.0..). The probability that a patient remained on that treatment in subsequent cycles was based on observational data that was not specific to the drug being considered (.0..). During any cycle, patients can move from any health state to death, the probability of which is determined by age and whether or not they are seizure free..0. Model comparators Based on the indications listed in the BNF, March 00, drugs licensed as monotherapy for focal epilepsy are carbamazepine (CBZ), gabapentin (GBP), lamotrigine (LTG), levetiracetam (LEV), oxcarbazepine (OXC), sodium valproate (VPA) and topiramate (TPM). The specific drugs included in the analysis were determined by the availability of clinical trial data on outcomes of efficacy and tolerability and fortunately, trial data was available for all the licensed drugs listed above. In addition to immediate-release carbamazepine, data was available for controlled-release carbamazepine. The GDG was interested in evaluating this formulation, thus it was included in the analysis..0. Treatment Effectiveness.0.. Estimation of response to and withdrawal from monotherapy during the first cycle The probabilities of treatment success during the first cycle of treatment were estimated from the clinical trial data specific to a given drug. For monotherapy, response was defined as the achievement of seizure freedom without withdrawal of treatment. Treatment failure was defined as either withdrawal caused by adverse events or a less than 00% reduction in seizure frequency. Patients that respond to therapy in that first cycle are assumed to continue therapy in subsequent cycles. The first probability hypothetical patients in the model faced was that of withdrawing due to adverse events (pwdae). To calculate pwdae for each individual AED, the odds ratio of a given AED compared to carbamazepine from the network meta-analysis was converted into a relative risk and applied to the absolute probability of withdrawal due to adverse events for carbamazepine using the following formula: pwdae = CBZ risk relative risk Where: Re lative risk = odds ratio ( CBZ risk ( odds ratio) ) Page of

6 0 0 The absolute probability of withdrawal due to adverse events for carbamazepine was calculated as part of the network meta-analysis presented in appendix O, and equals 0. (% CI 0.0 to 0.). For the probabilistic analysis, uncertainty in the risk parameter for carbamazepine was incorporated using a beta distribution and uncertainty in the comparative withdrawal estimates is incorporated by using 0,000 of the simulated odds ratios from the network meta-analysis. Using the simulated outputs allows us to preserve the joint posterior distribution from the network meta-analysis and any correlation of treatment effects. Hypothetical patients that did not withdraw due to adverse events next face a probability of response. To calculate the absolute probability of response to AED treatment, the same formulae were used to convert the odds ratio of a given AED compared to carbamazepine from the network meta-analysis into a relative risk and applied to the absolute probability of response with carbamazepine. The absolute probability of response for carbamazepine was calculated as part of the network meta-analysis presented in appendix O, and equals 0. (% CI 0.0 to 0.0). Because these response figures were based on intention to treat calculations, and they were being implemented in the model as conditional upon having not withdrawn due to adverse events, they were converted into conditional probabilities using the following method: Probabitlity ( response not withdrawn) = relative risk CBZrisk ( pwdae) For the probabilistic analysis, uncertainty in the risk parameter for carbamazepine was incorporated using a beta distribution and uncertainty in the comparative response estimates is incorporated by using 0,000 of the simulated odds ratios from the network meta-analysis. During probabilistic implementation of the model, it was rare but possible that the calculation of this conditional probability could return a value greater than one. This would happen if the probability of achieving seizure freedom was greater than the probability of not withdrawing due to adverse events. On the rare occasions when this happened it was assumed that 00% of patients not withdrawing due to adverse events experienced a response to treatment. Levetiracetam and controlled-release carbamazepine (CBZ-CR) did not connect to the network of evidence such that they could be evaluated with all other drugs in of the network meta-analysis. The GDG made the assumption that CBZ-CR is no different in terms of efficacy from immediate-release carbamazepine, but that it may have an advantage in terms of a lower incidence of intolerable side effects leading to withdrawal. Therefore, CBZ- CR and levetiracetam were built into the model using the probability of withdrawal due to adverse events for CBZ-CR from the trial data and fitting a log normal distribution to the odds ratio of levetiracetam compared to CBZ-CR. Based on the GDG assumption, the probability of response for CBZ-CR was assumed to be equal to that of normal release carbamazepine and a log normal distribution was applied to the observed odds ratio in the trial to determine response parameters for levetiracetam. Parameters of each distribution are presented for each drug in table. Page of

7 0 Table : Effectiveness parameters for AEDs used as monotherapy in the treatment of newly diagnosed focal epilepsy Withdrawal due to AE Achievement of SF Median odds ratio a Median odds ratio a psf not Drug (compared to CBZ) pwdae (compared to CBZ) withdrawn Source CBZ b NMA GBP NMA LTG NMA OXC NMA VPA NMA TPM NMA CBZ-CR c d 0. d (mean of logs=-0.; (mean of logs=-0.0; LEV 0. Std dev of Std dev of logs=0.) logs=0.) a Uncertainty incorporated using individual Markov chains from network meta-analysis b CBZ pwdae: mean = 0.; α=, β=. psf: mean=0.; α=; β= c CBZ-CR pwdae: mean = 0.; α=, β=. psf: mean =0.; α=; β= d Odds ratios for levetiracetam are compared to CBZ-CR Brodie 00; Assumption 0. Brodie 00; Assumption Estimation of response to monotherapy in subsequent cycles Observational data and clinical experience of the GDG indicated that for both monotherapy and adjunctive therapy, the probability of a patient changing treatment decreases as the time they have been on a given treatment increases. Therefore, it would be inappropriate to assume a constant probability of treatment failure based on the clinical trial data because it would overestimate treatment discontinuation. But it would also be inappropriate to assume that all patients would remain on treatment following the trial period as this would underestimate treatment discontinuation. The data used to inform the probabilities of treatment failure subsequent to the first cycle are discussed here for monotherapy and in section.0.. for adjunctive therapy. In the base case, the probabilities of treatment failure subsequent to the first cycle used in the base case were derived from drug-specific treatment failure data presented in SANAD. -month probabilities of treatment failure for carbamazepine, gabapentin, lamotrigine and topiramate during a follow-up of years were interpolated from a published graph. year follow-up data from SANAD was available for sodium valproate, but it was in a population comprised predominantly of patients with generalised epilepsy. Probabilities of treatment failure for oxcarbazepine were only available for a follow-up of years, but were also interpolated from the published graph. No data was available for levetiracetam as it had not been evaluated as part of SANAD. To inform the discontinuation parameters for years and for oxcarbazepine and for all years for levetiracetam, amalgamated treatment failure data for all SANAD drugs was used. It was assumed that no more discontinuations of treatment with any drug occurred after years. Uncertainty in these parameters was incorporated using beta distributions with parameters defined by intention to treat Page of

8 0 population figures from SANAD. Probabilities and beta distribution parameters are presented for each drug in table. Table : Long-term discontinuation probabilities and beta distribution parameters for AEDs used as monotherapy in the treatment of newly diagnosed focal epilepsy, from SANAD Duration of treatment (months) AEDs 0 0 CBZ alpha beta GBP alpha beta LTG alpha beta TPM alpha beta OXC* alpha beta VPA alpha beta LEV* alpha beta *Discontinuation data for all time points for LEV and for time points to months for OXC are interpolated from time to treatment failure graph for all drugs except for OXC, presented in Appendix of SANAD. 0 0 Although the data and assumptions informing the base case were not expected to bias the results in favour of or against any single AED, it was considered necessary to use an alternative data source in a sensitivity analysis. Long-term follow up data from the National General Practice Study of Epilepsy was interpolated from a published graph and used to calculate alternative probabilities of treatment failure over an year period. This data was not specific to the drug under consideration, but like the SANAD data, indicated that the probability of failure, and thus discontinuation, declined over time for patients who successfully completed the first cycle on any given therapy. Using this data, it was assumed that no more discontinuations of treatment occurred after years. Uncertainty in these parameters was incorporated using beta distributions with parameters defined by the observed population in the NGPSE follow-up study. These probabilities and their beta distribution parameters are presented for each drug in table. Figure graphically illustrates how all of these probabilities determine treatment retention over time. Table : Long-term discontinuation probabilities and beta distribution parameters for AEDs used as monotherapy in the treatment of newly diagnosed focal epilepsy, from NGPSE follow-up data Duration of Beta distribution -month probability therapy (months) parameters Page of

9 0 alpha beta Figure : Long-term discontinuation of monotherapy Estimation of response to and withdrawal from adjunctive therapy during the first cycle Patients who do not achieve seizure control or withdraw due to adverse events during the first cycle of treatment with monotherapy or who discontinue during a subsequent cycle are assumed to move on to adjunctive therapy. The probabilities of treatment success during the first cycle of treatment with an adjunctive AED were estimated from the clinical trial Page of

10 0 0 data specific to a given drug. For adjunctive therapy, response was defined as either the achievement of seizure freedom or between a 0% and % reduction in seizure frequency without withdrawal of treatment. Treatment failure was defined as either withdrawal caused by adverse events or a less than 0% reduction in seizure frequency. Patients that respond to adjunctive therapy in the first cycle are assumed to continue adjunctive therapy in subsequent cycles. The details of how response and withdrawals were calculated and incorporated into the model are presented in full in section... as part of the cost-effectiveness analysis of AED used as adjunctive treatment in adults with refractory focal epilepsy. In this analysis the comparison was between drugs used as monotherapy, thus a single AED was chosen to be a common adjunctive therapy across all comparators. Tiagabine (TGB) has the lowest estimated daily cost of possible adjunctive drugs that were not also being evaluated as monotherapy. Therefore, all patients who move on to adjunctive therapy are assumed to face the probabilities of response and withdrawal associated with tiagabine, which are presented in table. Table : Effectiveness parameters for tiagabine as adjunctive therapy in refractory focal epilepsy Drug pwdae p>0% reduction not withdrawn psf p>0% reduction TGB Estimation of response to adjunctive therapy in subsequent cycles Observational data and clinical experience of the GDG indicated that like for monotherapy, the probability of a patient changing treatment with adjunctive therapy decreases as the time they have been on a given treatment increases. The probabilities of treatment failure subsequent to the first cycle were interpolated from a published graph presented in Hawkins 00 based on the results of a tiagabine open-label follow-up study. This data was not specific to the drug under consideration and indicated that the probability of failure, and thus discontinuation, declined over time for patients who successfully completed the first cycle on any given therapy. As the observed study data was unavailable due to commercial in-confidence, uncertainty in the probabilities of treatment discontinuation was accounted for by using beta distributions for a hypothetical cohort of 00 patients. These probabilities and their beta distribution parameters are presented in table. Page 0 of

11 0 0 0 Table : Probabilities of long-term treatment discontinuation for AEDs used as adjunctive therapy* Duration of therapy (months) Probability Alpha Beta 0- m m m m m m m m m * Data interpolated from graph in Hawkins 00 which is based on results of an unpublished open-label follow-up study of TGB.0. Cost Data Only direct NHS costs were considered, including costs associated with starting and switching therapies, additional healthcare costs required for the treatment of seizure free and not seizure free epilepsy patients and costs of specific anti-epileptic drugs. The calculation of each cost component is described in detail in the following sections..0.. Initiating and switching to a new AED Previous published analyses estimated that on average one GP visit and one outpatient visit would be required when a patient starts or switches treatment with AEDs. The GDG felt that this was a gross underestimation and agreed on alternative estimates based on their own consensus opinion. Their estimates of healthcare resource use are summarised in table. Unit cost estimates applied to these estimates of resource use were taken from the PSSRU 0 and NHS reference costs. Table : Unit costs, health service use and total costs for starting and switching AED therapy Cost per Starting AED therapy Switching AED therapy Health Service use visit a Number Total Number Total ( ) of visits b cost ( ) of visits b cost ( ) GP 0 Neurology outpatient initial visit Neurology outpatient follow-up Phone-call follow-up Total cost per patient a Unit costs from PSSRU 0 and NHS reference costs b based on GDG consensus opinion.0.. Healthcare costs associated with seizure status Individuals with epilepsy access healthcare services more than individuals without epilepsy, but how much and how often they access services has been linked to whether they are Page of

12 experiencing seizures or not. Therefore, one element of cost differences between treatments will be determined by their effectiveness in controlling seizures. Wilby and colleagues looked at various methods to calculate these costs, and little new data has become available since they undertook their analysis. When they undertook their review of resource use estimates, they looked at two studies by Heaney and colleagues,, a study by Begley and colleagues and a study by Jacoby and colleagues. Heaney and colleagues and Begley and colleagues either assumed annual estimates GP visits and outpatient attendances or derived them from a panel of experts. Begley and colleagues based their estimates of resource use in the USA on expert opinion, but the results are not applicable to the NCGC model due to differences between UK patterns of care, age of the study and included patients. Jacoby and colleagues conducted a retrospective study in on a cohort of, UK adults and children with active epilepsy. Patients were asked to describe the frequency of contacts they had with various health services over the last year. Patients were not asked about length of hospital stays for inpatient episodes or whether outpatient visits were new referrals or follow-up visits. The SANAD randomised controlled trial comparing different AEDs used as monotherapy collected information about resource use as part of their economic evaluation, however the published data is not readily useable for inclusion in the NCGC model. It does not report data on resource use differentiated by seizure status, nor does it does it break down estimates between year one and year two of the study. For this analysis, a similar approach to the one taken by Wilby and colleagues was taken. Probabilities of service use are taken from Jacoby and colleagues and multiplied by an expected number of contacts estimated by the GDG. Jacoby and colleagues collected data for three groups of patients, those experiencing no seizures, less than one seizure per month and more than one seizure per month. Like Wilby and colleagues, our model does not differentiate between health states with more or less than one seizure per month. However, the difference in service use between these states, as reported by Jacoby and colleagues, is relatively small. The service use associated with experiencing more than one seizure per month is used in the model for all non-seizure free patients. The unit costs for inpatient stays, A&E episodes and outpatient attendances were each calculated as a weighted mean of many NHS reference cost components. Relative weights applied to each component were based on the activity level reported in the NHS reference cost schedule We assumed that the interquartile range for any given NHS reference cost fit a gamma distribution. Based on that assumption, we took the mean and manually adjusted the standard error estimate to calculate alpha and beta parameters for a gamma distribution that would come closest to reproducing the interquartile range reported in the NHS reference costs schedule. For the probabilistic analysis, each cost component was varied, multiplied by its relative weight and then summed with other cost components to equal the total unit cost for a given service. The unit cost of a GP consultation was taken from PSSRU 0 and is assumed to be constant. The probabilities of service use, expected number of contacts, unit costs of health services and calculated total costs are summarised in table. Page of

13 0 Table : Average yearly cost of seizures Probability of use a Health service use Seizure free a Not seizure free b Expected no of visits given nonzero use c Unit cost d Seizure free (=a*c*d) Cost ( ) Not seizure free (=b*c*d) Inpatient , A&E Outpatient (first visit) Outpatient (follow-up) GP Expected total cost per adult patient 0 a,b Annual probability of accessing a service dependent upon seizure status, from Jacoby and colleagues c Based on GDG consensus opinion d Unit costs from PSSRU 0 and NHS reference costs Specific AED costs Differences in the cost of individual AEDs are likely to be a determining factor in the total costs of each treatment option, and this parameter is driven by the assumption of dose used. Drug costs were calculated by estimating the average daily dose in milligrams and multiplying it by a weighted mean unit cost per milligram. Uncertainty in drug doses was incorporated using a gamma distribution with parameters based on the minimum and maximum recommended doses provided in the BNF ii, September 00. A weighted mean, based on relative usage data from the Prescription Cost Analysis and unit costs from the BNF, was calculated for the unit cost per milligram of each individual AED iii. For details on the weights applied and the calculation of the mean cost per milligram, see appendix Q. Daily doses, weighted mean unit costs, and total drug costs per -month cycle for each modelled AED are presented in table. Drug Table : Costs of individual AEDs Recommende d min dose (mg/day) Recommended max dose (mg/day) Cost /mg ( ) Implied mean dose (mg/day) Manually adjusted SE Gamma parameters of mean dose Alpha Beta Mean - month cost ( ) CBZ LTG LEV TPM VPA ii This method was not used to estimate the dose of levetiracetam, as it would result in an over-estimation of the mean daily dose (000 mg). Instead, we used a mean daily dose of mg, reported by UCB Pharma during consultation and agreed by the GDG. We assumed a minimum and maximum daily dose of 000 mg and 00 mg, respectively. iii This exact costing method could not be used for topiramate, because the cost for non-proprietary topiramate was available only on the NHS Electronic Drug Tariff (accessed 0 October 00). Therefore, the weighted average unit cost of topiramate was calculated using data from the Prescription Cost Analysis, the BNF (for branded Topamax) and the NHS Electronic Drug Tariff (for non-proprietary topiramate). Page of

14 0 OXC GBP CBZ-CR Carbamazepine was assumed to be the concomitant AED in the adjunctive therapy phase of the model. The cost of treatment in the maintenance phase of the model, for patients who have failed adjunctive treatment with comparator AEDs, is assumed to be equal to that of an older AED used as monotherapy. Because carbamazepine is the most commonly prescribed older AED for focal epilepsy, it was used as the maintenance drug in the model..0.. Exclusion of costs associated with adverse events or side effects The costs of adverse events are challenging to estimate because accurate estimates require both the incidence of adverse events and the treatment consequences. Because most adverse events can be assumed to disappear with either dose adjustment or withdrawal of treatment and can be expected to require no further treatment, they were not considered a priority area for inclusion in the model. The GDG considered the risks of possible adverse events qualitatively, but did not feel that the model was lacking for not having looked at specific adverse events..0. Utilities (health-related quality of life) The QALYs gained for patients in each health state were calculated based on the utility estimates associated with being seizure free, having a 0-% reduction in seizure frequency or not responding to therapy (i.e. continuing to have uncontrolled seizures). Uncertainty in these estimates was incorporated using a gamma distribution parameterised as a function of the mean and standard deviation of these estimates. The utility weights were derived from Selai and colleagues, a non-randomised audit of patients starting a new adjunctive AED who completed the EQ-D questionnaire after months, and was supported by EQ-D estimates measured in the focal epilepsy population in the SANAD trial. Selai and colleagues reported that responders who were seizure free at the end of six months had a utility weight of 0., who experienced less than one seizure per month had a utility weight of 0., more than one seizure per month had a weight of 0. and had no response as 0.. When Wilby and colleagues interpreted this data for use in their model, they combined the groups having less than and more than one seizure per month and estimated their utility to be 0.0, thus overcoming the paradox of utility weights measured in the study (i.e. people experiencing more than seizure per month having a lower utility weight than people experiencing less than seizure per month). The difference in utility between seizure free responders and non-responders was 0.. SANAD measured EQ-D scores at various points during its two-year follow up and showed that there were not significant differences between treatment arms. However, it did show that EQ-D scores were significantly different between responders (i.e. patients who achieved a -month remission from seizures) and non-responders (i.e. patients who had withdrawn from treatment by -year follow-up). In the predominantly focal epilepsy study Page of

15 0 population (Arm A), the responders to treatment reported an average 0.0 QALY gain (0.0 to 0.) while the non-responders reported an average 0.0 QALY loss (0.0 to 0). The difference between the groups was thus 0.. Given the above data, the health state utilities derived by Wilby and colleagues from Selai and colleagues is supported by the data from Marson and colleagues, and on that basis, the same data was used to parameterise health state utilities in our model. Uncertainty in the estimates was incorporated using a gamma distribution around the disutility estimates between health states. Mean utilities, disutilities and distribution parameters are presented in table. 0 Table : Parameters used to estimate health state utilities based on data from Selai and colleagues and Marson and colleagues Parameter description (parameter name) Utility for a seizure free patient (U SF ) Utility for a patient who has between 0-% reduction in seizure frequency (U 0-% reduction ) Utility for a patient who does not respond to therapy (U NR ) Value: mean (SE) 0. (0.0) 0.0 (0.00) 0. (0.0) Disutility: mean (SE) 0.0 (0.0) 0.0 (0.0) 0.0 (0.0) Gamma distribution parameters Alpha Beta Calculation U SF. 0.0 U SF -disutility 0-% reduction U 0-% reduction -disutility NR Utility associated with dead state (U dead ) 0 0 constant Mortality Patients who achieved seizure freedom with treatment faced the age-dependent death rate associated with experiencing no seizures. Patients who experienced a 0-% reduction or no response to treatment faced age-dependent death rates associated with experiencing seizures. To calculate the standardised mortality ratio of people with epilepsy, the model used observed deaths in the NGPSE study,0 and expected deaths from Mortality Statistics. The NGPSE study calculates the hazard ratio for people with epilepsy without seizures compared with patients with epilepsy with seizures to be 0.. Assuming 0% of patients in the NGPSE study are seizure free, death rates for patients with and without seizures are calculated using the following identities: Death rate SF Death rate general population *SMR SF Death rate NSF Death rate general population *SMR NSF SMR epilepsy SMR notsf *[-p(sf)] + SMR SF *p(sf) Hazard ratio SMR SF /SMR NSF = 0. Where p(sf) is the probability a patient is seizure free. Page of

16 0 0 0 The resulting estimates of death rates for patients with and without seizures are as shown in table 0. Table 0: Mortality rates for seizure free and not seizure free patients Age (years) Death Rate General Population (per 000) SMR Epilepsy SMR not SF SMR SF Death Rate NSF (per 000) Death Rate SF (per 000) 0 to to to to Computations The model was constructed in TreeAge Pro 00 and was evaluated by cohort simulation. All patients start monotherapy with an AED in the first cycle and either respond (seizure free), do not respond or withdraw due to adverse events. Movement between health states in subsequent cycles is determined by the various probabilities described in the preceding sections. Each -month cycle the cohort spends in a given health state is counted. Total QALYs were calculated from the above information as follows. Each -month cycle, the time spent in each health state of the model was weighted by the utility for that state. The QALYs per cycle were then discounted to reflect time preference. QALYs during year one were not discounted. The total discounted QALYs was the sum of the discounted QALYs per cycle. Total discounted QALYs = i t= Q( t) t ( + r) Where: t=cycle number; i=maximum cycle number; Q(t) = QALYs in cycle t; r = discount rate Total costs were calculated from the above information as follows. Each cycle, the time spent in each state of the model was multiplied by the costs for that state. The costs per cycle were then discounted to reflect time preference. Costs during year one were not discounted. The total discounted costs were the sum of the discounted costs per cycle. Total discounted cos ts = i t= C( t) t ( + r) Where: t=cycle number; i=maximum cycle number; C(t) = costs in cycle t; r = discount rate The used cost-effectiveness metric is the incremental cost-effectiveness ratio (ICER). This is calculated by dividing the difference in costs associated with two alternatives by the difference in QALYs. The decision rule then applied is that if the ICER falls below a given cost per QALY threshold, the result is considered to be cost-effective. If both costs are lower and QALYs are higher, the option is said to dominate and an ICER is not calculated. Page of

17 Costs ICER = QALYs ( B) Costs( A) ( B) QALYs( A) When there are more than two comparators, as in this analysis, options were ranked in order of increasing cost and then options ruled out by dominance (i.e. those that were more costly and less effective than alternate strategies) or extended dominance (i.e. where a linear combination of other strategies could produce greater benefit at lower cost) were excluded before calculating ICERs. ICERs were calculated based on mean costs and effects as estimated during the probabilistic implementation of the model. Results are presented on the cost-effectiveness plane where the total cost and total QALYs are plotted for each treatment option. The least cost monotherapy AED is located at the origin, defined as the intersection between its total QALYs (on the x-axis) and total cost (on the y-axis). Comparisons not ruled out by dominance or extended dominance are joined by a line on the graph where the slope represents the incremental cost-effectiveness ratio, the value of which is labelled. The effect of uncertainty in the results is reflected by the reporting of % confidence intervals around mean total costs and effects. Secondly, uncertainty was illustrated by estimating the probability a given AED was the optimal treatment option. For strategy X, this was calculated as Net Benefit ( X ) = ( QALYs( X ) D) Costs( X ) Where: Costs/QALYs(X) = total discounted costs/qalys for option X; D=threshold The decision rule then applied is that the strategy with the greatest net benefit is the costeffective option at that threshold. That strategy is expected to provide the highest number of QALYs at an acceptable cost. The probability a given AED is optimal is calculated as the proportion of simulations where that option had the greatest net benefit at the specified threshold..0. Sensitivity analysis In addition to the probabilistic analysis run to take account of uncertainty around the input parameters, various other sensitivity analyses, where one or more inputs were varied, were undertaken to test how sensitive conclusions of the analysis are to model assumptions and data sources. First, a scenario analysis in which sodium valproate was removed was undertaken to identify the most cost-effective strategy for individuals for whom sodium valproate is contraindicated or unsuitable. Another scenario analysis was undertaken to test the cost-effectiveness of drugs in the less common, but clinically possible, situation where all but gabapentin and levetiracetam are contraindicated. Next, alternative data from the NGPSE was used to parameterise long term rates of discontinuation. A series of sensitivity analyses were run to test how variation in drug costs might affect the basecase results. In particular, the model was run using costs for brand name carbamazepine (Tegretol), costs for brand name lamotrigine (Lamictal), non-proprietary lamotrigine and non-proprietary topiramate. Given that levetiracetam is expected to be generically produced within months of the guidelines being published, the GDG was interested to see Page of

18 0 0 0 how the relative cost-effectiveness of levetiracetam might change with a reduction in its unit cost. Therefore, we tested the sensitivity of the model results to a reduction in the cost of levetiracetam by 0%, 0% and 0%. The hypothetical cohort modelled in the base case was 0 years old at initiation of therapy. The GDG felt that although this is not an unreasonable age on which to base decisions, many people diagnosed with focal epilepsy will be much younger, and thus start AED therapy earlier. The model was therefore run for a younger hypothetical patient group, starting at age 0 years, in order to see how age, and therefore lower risk of death, might influence the cost-effectiveness of different AEDs. A -year time horizon was used in the base case, but the GDG recognised that many patients will be on therapy for shorter or longer periods. Therefore, in a series of sensitivity analyses, the model was run for 0 cycles ( years) and 0 cycles (0 years). Results Results of the basecase analysis and various other scenarios are detailed in the following sections. All results are means from the probabilistic analysis unless otherwise specified... Basecase Analysis Results of the basecase incremental analysis are presented in table in order of increasing total cost per patient. The health gain to individuals is presented in terms of total QALYs for each alternative AED as well. Table : Incremental analysis of basecase results Drug Total Cost ( ) Incremental Cost ( ) Total QALYs Incremental QALYs ICER ( /QALY) VPA,. CBZ-CR,, Dominated CBZ,, Dominated LTG,,. -0. Dominated GBP,,. -0. Dominated OXC 0,0, Dominated LEV,0, Dominated TPM,, Dominated Sodium valproate has the lowest mean total cost (,) and also the greatest benefit (. QALYs). Based on the estimated means, treatment of all patients with sodium valproate dominates other AED therapies. These results are presented graphically, on the cost-effectiveness plane, in figure. Page of

19 0 Figure : Basecase results on the cost-effectiveness plane 0 A breakdown of costs and QALYs is presented in table. Based on this breakdown, it is clear that the largest component of total costs for the treatment strategies is the cost of the drugs themselves. Drug costs (monotherapy, adjunctive therapy and maintenance therapy) make up more than half of the total cost for all strategies, except sodium valproate where they make up %. The more expensive the strategy is overall, the greater the proportion of the total cost is made up of drug costs. % of total cost for lamotrigine is made up of the cost of drugs alone, whereas % of the total cost of levetiracetam is made up of drug costs. Outpatient costs represent the next largest component of total cost for all strategies. Table : Breakdown of basecase results VPA CBZ-CR CBZ LTG GBP OXC LEV TPM Total QALYs Life years Undiscounted life years Total Costs,,,,, 0,0,0, Drug costs,0,,,,0,,, Inpatient costs,0,00,,,0,0,0 Outpatient costs,,,,0,,,, Primary care costs A&E costs 0 The probabilistic results of the base case analysis are presented in table, with % credible intervals around mean cost and QALY estimates and net benefit calculations and probabilities of each AED being optimal at thresholds of 0,000 and 0,000 per QALY. Page of

20 0 0 Table : Summary of basecase probabilistic results AED VPA CBZ-CR CBZ LTG GBP OXC LEV TPM Mean total cost (% CI) ( ), ( to ), ( to 00), (0 to 0), ( to 00), ( to ) 0,0 ( to 0),0 ( to 0), ( to ) Mean total QALYs (%CI). (. to.). (. to.). (. to.). (. to.0). (. to.0).00 (. to.0). (. to.0). (. to.) Net benefit (probability AED is most cost-effective) (threshold = 0K per QALY) Net benefit (probability AED is most cost-effective) (threshold = 0K per QALY), (.%), (.0%), (.%),0 (.%) 0, (0.0%), (0.0%), (0.%), (0.0%), (0.0%), (0.0%), (0.%), (0.%), (0.0%), (0.%), (0.0%),0 (0.0%) The probabilistic results show that sodium valproate is the optimal first-line AED in % of simulations at willingness to pay thresholds of 0,000 and 0,000 per QALY gained.. Based on these results, there is very little uncertainty in a decision to recommend sodium valproate as an initial treatment... Sensitivity analyses... Exclusion of sodium valproate due to contraindication or unsuitability Sodium valproate, although an extremely effective AED, is contraindicated in some patients and is often avoided in women of child-bearing potential due to its teratogenic effects. Therefore, it is important to evaluate what the most cost-effective alternative in this group might be. The results of this scenario, where sodium valproate is removed from the list of monotherapy options, are presented in table and figure. According to the results, controlled-release carbamazepine has the lowest total cost and oxcarbazepine has the greatest effect. Controlled-release carbamazepine is more effective and less costly than all alternatives except for oxcarbazepine. However, the additional benefit of oxcarbazepine compared to controlled-release carbamazepine does not justify its additional cost (ICER = 0,) given a willingness to pay threshold of 0,000 per QALY. Page 0 of

21 0 Table : Incremental analysis of results excluding sodium valproate Drug Total Cost ( ) Incremental Cost ( ) Total QALYs Incremental QALYs ICER ( /QALY) CBZ-CR,. CBZ, Dominated LTG, Dominated GBP,. -0. Dominated OXC 0,0, , LEV, Dominated TPM,, Dominated Figure : Results for scenario where sodium valproate is contraindicated on the cost-effectiveness plane 0 Table presents the mean cost and mean QALYs for all AEDs except sodium valproate, along with the probability of each being the optimal strategy at thresholds of 0,000 and 0,000 per QALY gained. Based on these results, at 0,000 per QALY the combined probability that carbamazepine, normal or controlled-release formulation, is optimal is %. Oxcarbazepine is most cost-effective in % of simulations and lamotrigine in %. At a threshold of 0,000 per QALY, carbamazepine has a combined probability of 0% and oxcarbazepine has a probability of % of being the most cost-effective AED. At both thresholds, topiramate and levetiracetam have a less than % probability of being optimal and gabapentin has a less than % probability of being optimal. Page of

22 0 Table : Summary of results for scenario where VPA is contraindicated AED Mean total cost (% CI) ( ) Mean total QALYs (%CI) Probability AED is most cost-effective (threshold = 0K per QALY) Probability AED is most cost-effective (threshold = 0K per QALY) CBZ-CR CBZ LTG GBP OXC LEV TPM, ( to 00), (0 to 0), ( to 00), ( to ) 0,0 ( to 0),0 ( to 0), ( to )... NGPSE follow-up data. (. to.). (. to.). (. to.0). (. to.0).00 (. to.0). (. to.0). (. to.).%.%.%.%.%.% 0.% 0.%.%.% 0.%.% 0.%.% 0 In the base case, data from SANAD was used to parameterise the long-term discontinuation rates following response to initial treatment. Drug-specific data was available for most drugs, but not all and in the absence of data, assumptions were made. To test how sensitive the results and conclusions of the base case are to this source of data and the assumptions made we used alternative long-term data from a UK observational study and applied it uniformly to all modelled drugs. The results of this scenario are presented in table. Table : Incremental analysis of results using NGPSE follow-up data Total Cost Incremental Total Incremental ICER Drug ( ) Cost ( ) QALYs QALYs ( /QALY) VPA,. CBZ-CR,00, Dominated CBZ,, Dominated LTG,,.0-0. Dominated GBP,,. -0. Dominated OXC 0,, Dominated LEV,, Dominated TPM,, Dominated All costs reduce slightly using this data, and all effects increase. The results show that this data does not change the conclusion of the base case, which is that sodium valproate is the least cost and most effective first-line AED to treat adults with newly diagnosed focal epilepsy. Using this data, if sodium valproate were contraindicated, as in the previous Page of

23 scenario, controlled-release carbamazepine is least cost and most effective, generating more QALYs than oxcarbazepine..... Contraindications Contraindications and possible drug interactions mean that some AEDs should not be considered in the first-line treatment of some patients. Drugs such as carbamazepine, lamotrigine, oxcarbazepine and topiramate interact with oral contraceptives, in ways that can reduce the effectiveness of one or the other. There are significant concerns regarding the potential for teratogenicity with sodium valproate, and evidence is mounting to warrant the same concerns about topiramate. There are high risks of rash or allergic reactions to carbamazepine in certain groups. Lamotrigine must be titrated up very slowly and therefore if rapid control of seizures is required, it may not be the optimal drug. If these contraindications / considerations are present, what alternative AED is cost-effective and should be offered? For this scenario only gabapentin and levetiracetam remain in the analysis and the incremental results are presented in table. Table : Incremental analysis of results excluding CBZ, LTG, OXC and VPA Total Cost Incremental Total Incremental ICER Drug ( ) Cost ( ) QALYs QALYs ( /QALY) GBP,. LEV,0,. 0., In this scenario, gabapentin is the least costly and least effective AED and levetiracetam is the most costly and most effective. Given a willingness to pay of 0,000 per QALY gained, levetiracetam is cost-effective with an incremental cost-effectiveness ratio of, compared to gabapentin.... Additional scenario analyses The model was rerun to reflect several different scenarios, each designed to test a particular assumption. In all scenarios, sodium valproate remained the most effective and least costly AED. Therefore, the results of the various scenario analyses are presented within the scenario where sodium valproate is contraindicated. In the base case, a weighted average cost per milligram of carbamazepine (combining nonproprietary and branded products) was used. The calculations for unit cost indicate that the non-proprietary carbamazepine is quite a bit costlier per milligram than the branded carbamazepine, Tegretol. Therefore, in a sensitivity analysis, the unit cost per milligram of Tegretol (branded carbamazepine) was used in order to test how this different cost might affect its cost-effectiveness and rank overall. When this lower unit cost is used, immediaterelease carbamazepine is less costly than controlled-release carbamazepine, but controlledrelease carbamazepine is still likely to be cost-effective (ICER =,) compared to the immediate-release Tegretol. Oxcarbazepine is still more effective, but its additional costs Page of

24 outweigh its benefits compared to either carbamazepine formulation given a willingness to pay of 0,000 per QALY. Using base case estimates of cost, all other scenarios modelled showed first-line treatment with controlled-release carbamazepine to be the least cost strategy, dominating all other strategies except for oxcarbazepine. However, the incremental cost effectiveness of oxcarbazepine compared to controlled-release carbamazepine never drops below the 0,000 per QALY willingness to pay threshold. In the scenario where the cost of lamotrigine is assumed to be that of non-proprietary products only, lamotrigine is less costly than carbamazepine, but carbamazepine s comparative incremental cost effectiveness is under the threshold at,. Its probability of being cost-effective at a 0,000 per QALY threshold when sodium valproate is contraindicated went up to % from % in the base case. When the cost of lamotrigine is assumed to be that of brand name Lamictal, lamotrigine becomes the most costly first line AED strategy overall and is dominated by all other alternatives except gabapentin and topiramate. Its probability of being cost-effective went down to less than % in this scenario. When the cost of topiramate is assumed to be that of non-proprietary products only, topiramate is the third most costly strategy and still second least effective. Its probability of being cost-effective at a 0,000 per QALY threshold when sodium valproate is contraindicated went from less than % in the base case to.%. When the least costly version of each drug product is used iv (i.e. generic lamotrigine and topiramate and branded carbamazepine), results show a reduction in the mean total costs of all strategies but lead to the same conclusions as the base case, wherein controlledrelease carbamazepine is most likely to be optimal. Incremental cost-effectiveness for carbamazepine and oxcarbazepine increased when the cohort starting age was taken from 0 years down to 0 years. Controlled-release carbamazepine is still the most cost-effective at a 0,000 per QALY threshold of all drugs when sodium valproate is excluded. The model was run for shorter ( and 0 years) and longer (0 years) to test how the costeffectiveness of drugs might be sensitive to the time horizon modelled in the base case. The results indicate that the cost-effectiveness of drugs improve the longer the model is run. This is due to the high costs accrued early on as patients either switch drugs following an initial non-response to treatment or switch drugs a few years later. Fewer and fewer patients are expected to switch from the monotherapy to adjunctive drug and adjunctive to maintenance therapy as time goes on. iv Although brand and non-proprietary products are available for both gabapentin and oxcarbazepine, using either of these alternatives will make no difference to their cost or ranking. The cost of non-proprietary oxcarbazepine and brand name Trileptal is the same. And though there is a difference in cost between nonproprietary gabapentin and brand name Neurontin, the Prescription Cost Analysis figures show that.% of prescribing is for generic gabapentin, thus the weighted average cost is almost entirely comprised of the less costly generic. Page of

25 0... Alternative costing scenarios for newer AEDs 0 0 AED The conclusions of this model, whilst in line with the results of Hawkins (00),, are slightly at odds with the results from SANAD, where in both oxcarbazepine and lamotrigine were shown to be likely cost-effective first line AEDs compared to carbamazepine. The trial-based economic evaluation, whilst of shorter duration ( years), may have captured impacts on quality of life that the present analysis is unable to account for. Therefore, in the following scenarios, we make the assumption that lamotrigine is the least cost AED by removing carbamazepine, controlled- and immediate-release formulations, along with sodium valproate and compare only the remaining newer drugs. Table presents the results of a series of different scenarios wherein alternative costing assumptions are used. As reported above, the model was run using costs for nonproprietary lamotrigine and topiramate, resulting in each of these strategies becoming less costly than in the base case. In the base case, when sodium valproate and carbamazepine are removed, oxcarbazepine has the greatest probability (%) of being optimal at 0,000 per QALY gained, followed closely by lamotrigine (%). When the cost of generic lamotrigine was used in the model, lamotrigine becomes the most likely to be optimal (%), followed closely by oxcarbazepine (%). Table : Probabilities for newer drugs being optimal in different costing scenarios Base Case Probability most cost-effective at willingness to pay threshold of 0,000 per QALY gained Least cost* Percent reduction in cost of LEV Least cost* and 0 0 percent 0 percent 0 percent percent reduction in cost of LEV Least cost* and 0 percent reduction in cost of LEV OXC.%.%.%.00%.0%.% 0.0% LTG.0%.%.%.0%.0% %.% GBP 0.% 0.0% 0.% 0.% 0.% 0.% 0.% LEV.%.%.%.00%.%.%.% TPM.%.0%.%.0%.0%.%.% *Least cost costs for non-proprietary lamotrigine and topiramate 0 The GDG was also interested in exploring how a change in the cost of levetiracetam might affect its cost-effectiveness compared to other newer AEDs. This was particularly important to explore given that levetiracetam has recently lost its patent protection and therefore generic products are expected to enter the market within months of this guideline s publication. However, given that generic levetiracetam is not yet on the market, no costs are available. We therefore performed a sensitivity analysis on levetiracetam s unit cost, assuming that it falls by 0%, 0% and 0%. The intention was to identify a point at which levetiracetam becomes cost-effective compared to other newer AEDs, such as lamotrigine and oxcarbazepine. Table presents the results of this sensitivity analysis, wherein the cost of levetiracetam is reduced. When all other things remain the same as in the base case and the cost of levetiracetam is reduced by 0%, oxcarbazepine and lamotrigine still have the greatest and second greatest probabilities of being optimal. But, levetiracetam is also optimal in just Page of

26 over one-quarter of simulations, up from % in the base case. At a 0% reduction in unit cost, levetiracetam surpasses both oxcarbazepine and lamotrigine, with the highest probability of being optimal (%). If the cost of levetiracetam reduces by as much as 0% with the introduction of generic competition, it is likely to be optimal in % of simulations compared with other newer AEDs. It is worth noting that at this price, it has a 0% probability of being optimal when compared to normal and controlled-release carbamazepine as well. In the final scenarios presented in table, the model was run assuming the least costly products were used (i.e. generic lamotrigine and topiramate) and that the cost of levetiracetam dropped by 0% and 0%. At a reduction of 0% in the cost of levetiracetam, the probability of being most cost-effective was closely split between oxcarbazepine, lamotrigine and levetiracetam. But at a 0% reduction in cost, levetiracetam was more than twice as likely as either oxcarbazepine or lamotrigine to be optimal. Although levetiracetam, at its current cost, is unlikely to represent good value for money, these various scenarios highlight the uncertainty in a decision to recommend any of oxcarbazepine, lamotrigine or levetiracetam over one another in the future. The probabilities that each is most cost-effective under these assumptions are very close and make it difficult to justify a recommendation of one over another. Discussion The economic evidence for first-line drugs used to treat newly diagnosed focal epilepsy came from two studies,, both of which had limitations preventing their direct application to the guideline decision context. Both used costs which were current at the time of evaluation, but which have since changed substantially, and neither included all the comparators of interest to the GDG. Therefore, the aim of this analysis was to fill in the gaps left in the published literature by evaluating the relative cost-effectiveness of different licensed AEDs used in the treatment of adults with newly diagnosed focal epilepsy. The base case analysis and all sensitivity analyses demonstrate the effectiveness and costeffectiveness of sodium valproate as the optimal drug to try first in this group of patients. Based on the data that parameterised the model, sodium valproate was shown to be the least expensive and most effective AED evaluated in the model. It is worth noting that evidence from an individual patient data (IPD) network meta-analysis (NMA) undertaken by the Tudor-Smith and colleagues showed carbamazepine to be superior to sodium valproate in the treatment of newly diagnosed focal epilepsy. The results of a subgroup analysis in patients with focal epilepsy showed that there was no difference between carbamazepine and sodium valproate on the outcome of time to treatment withdrawal, but did show a significant difference slightly favouring carbamazepine on the outcomes of time to -month remission from seizures and time to first seizure. The same IPD NMA showed broad similarity between sodium valproate and other drugs (lamotrigine, topiramate, gabapentin) on the efficacy outcome of time to - month remission from seizures. And on the outcome time to first seizure, sodium valproate appears broadly similar to lamotrigine and gabapentin, and trends towards being worse than both topiramate and oxcarbazepine. Although no formal sensitivity analysis was Page of

27 undertaken to explore the difference this data might have made, it is likely that sodium valproate would still have emerged as a reasonable treatment option given its low acquisition cost and similar or even better efficacy compared to other newer AEDs. Although sodium valproate emerged as the most cost-effective first-line AED, the GDG was clear that it is not an appropriate drug for all patients. Therefore, it was necessary to consider alternative first-line AEDs where this might be the case. In this situation, where sodium valproate is contraindicated, carbamazepine is likely to represent the most effective and cost-effective alternative. The costs calculated for controlled-release carbamazepine were less than those for normal release carbamazepine, and because it had a lower risk of withdrawal due to adverse events, it came out as the most cost-effective alternative in the base case. When the cost of branded carbamazepine (Tegretol) was used, immediaterelease carbamazepine was less costly than the controlled-release formulation. The GDG used these results, along with patient views, to inform their recommendation that if carbamazepine was being offered as a treatment, controlled-release formulations should be considered. Only in the situation where all but gabapentin and levetiracetam are contraindicated or carry serious concerns for drug interaction, levetiracetam is cost-effective. Limitations The analysis presented here has several limitations and the GDG took them into consideration as they used the results in the development of guideline recommendations. Firstly, the analysis did not take account of any specific adverse events associated with particular drugs. It incorporated issues of tolerability by including trial-reported outcomes on withdrawal due to adverse events, which the GDG felt was broadly sufficient given that most adverse events can either be mitigated through dose adjustment or will disappear once the drug is discontinued. The short-term effects of modelled AEDs were based on the results from a network metaanalysis of clinical trial data for outcomes of efficacy and tolerability. All drugs were included in the network meta-analysis with the exception of levetiracetam and controlledrelease carbamazepine. As this comparison did not connect to the network of direct evidence (described in appendix O), it had to be incorporated into the decision model separately and evaluated indirectly. The assumption was made that controlled release carbamazepine and normal release carbamazepine were equally effective; therefore, the relative effectiveness between levetiracetam and controlled-release carbamazepine defined the relative effectiveness between levetiracetam and normal release carbamazepine. Until levetiracetam or controlled-release carbamazepine is directly compared in a clinical trial to another drug in the network, this limitation will stand. The GDG did not view it as a major limitation to the conclusions of the analysis. The data used to model the drug-specific long-term effectiveness of AEDs used as monotherapy was limited to those that had been evaluated in SANAD, and even where this data was available from SANAD, it was not necessarily the most complete (in the case of oxcarbazepine) or applicable (in the case of sodium valproate). Levetiracetam was not evaluated as part of SANAD and thus the data for all drugs was used to inform its long-term Page of

28 probabilities of discontinuation. The results of the sensitivity analysis wherein NGPSE data was applied for all drugs did not change the overall results of the analysis, therefore, this limitation was not considered to have a significant impact on interpretation or conclusions of the analysis. The availability of utility data to inform the estimation of QALYs was quite limited. The EQ- D data from Selai and colleagues was gathered from a mixed epilepsy population, that is some patients had focal epilepsy and some had generalised epilepsy. However, the differences between health state utility estimates measured in Selai and colleagues were supported by limited information from SANAD which showed that the difference in benefit between responders (patients who achieved -month remission of seizures) and nonresponders (patients who failed treatment) at years follow-up was 0. QALYs. For future modelling work in the area of epilepsy, it might be useful for Marson and colleagues to translate their EQ-D estimates into health-state utilities focused around seizure status. Additionally, the development of an algorithm to map epilepsy-specific quality of life outcome measure onto a preference-based generic measure could be very useful for future health economic work in epilepsy. Conclusion Based on the clinical and health economic evidence, the GDG concluded that carbamazepine, lamotrigine, oxcarbazepine and sodium valproate were reasonable drugs to offer as first-line treatment in individuals with newly diagnosed focal epilepsy. This recommendation was based on the evidence from the model presented here as well as the evaluation performed by Marson and colleagues as part of SANAD. SANAD indicated that oxcarbazepine is the most effective and cost-effective first line AED both in terms of QALYs gained and total seizures avoided. The GDG also considered that although lamotrigine was not shown to be cost-effective compared to oxcarbazepine, the significant reduction in its unit cost since it was evaluated in the study is likely to improve its relative cost-effectiveness on both of these outcomes. In the same study, carbamazepine was the least cost and second least effective drug in terms of QALYs, but was the second most effective drug in terms of controlling seizures. Although the time horizon on the SANAD economic evaluation was relatively short ( years), it is likely to have captured much of the substantial resource use that comes with initiating treatment in a population with newly diagnosed epilepsy. Furthermore, it may have picked up impacts on health related quality of life arising from specific drug side effects, something that was not included in our analysis. The evidence to recommend carbamazepine and sodium valproate comes largely from the original analysis presented here, which was an update to the model presented by Hawkins (00). The results indicated sodium valproate to be a very cost-effective first line AED for focal epilepsy and that carbamazepine, either in normal or controlled release formulation, was likely to be an effective alternative if sodium valproate was contraindicated. Gabapentin, topiramate and levetiracetam were not shown to be cost-effective in comparison with the aforementioned drugs in the base case. Only in a scenario analysis where carbamazepine, lamotrigine, oxcarbazepine, sodium valproate and topiramate are all contraindicated were gabapentin or levetiracetam likely to be cost-effective alternatives. Page of

29 0 0 The GDG considered that in the situation where all first line drugs are contraindicated, levetiracetam would represent the best, most cost-effective choice. A series of sensitivity analyses were used to inform the GDG about the relative costeffectiveness of levetiracetam if and when its unit cost drops with the introduction of generic competition later in 0. The GDG used the sensitivity analyses to determine at what reduction in price is levetiracetam likely to be considered cost-effective compared to other drugs recommended as first-line treatments. Sodium valproate and controlledrelease carbamazepine were most cost-effective no matter how much the price of levetiracetam dropped. But, the results of the probabilistic analysis showed that at a 0% reduction in unit cost a decision between levetiracetam, lamotrigine and oxcarbazepine would be difficult to justify given that they were optimal in a similar number of model simulations Therefore, the GDG incorporated this information into a recommendation for the restricted use of levetiracetam now, such that commissioners of services and prescribers will know that levetiracetam may be cost-effective in a general population with newly diagnosed focal epilepsy in the future, if and when generic products become available, and its cost is reduced by approximately 0%. Page of

30 Economic Evaluation of AEDs used as adjunctive therapy in the treatment of adults with refractory focal epilepsy Introduction The review of clinical evidence for AEDs used as adjunctive therapy in adults with refractory focal epilepsy showed that most drugs were significantly more effective than continued monotherapy (placebo), but were almost all less well tolerated. A network meta-analysis was undertaken to simultaneously compare virtually all the AEDs assessed in the direct comparative data. Relative treatment effects estimated in the network meta-analysis showed a general similarity between different adjunctive drug options, but results also showed carbamazepine and sodium valproate, two very effective first line drugs, to be the most effective adjunctive drugs in more than % of Monte Carlo simulations. Health economic evidence for AEDs as adjunctive therapy in adults with refractory focal epilepsy was limited to two model based cost-utility analyses,, one of which was undertaken to inform previous NICE guidance (TA). Both studies were of good quality, but each had limitations that required further analysis to be undertaken. The model developed by Hawkins and colleagues estimated treatment effects from a network metaanalysis of clinical trials that met the inclusion criteria of their systematic review. However, a few of the studies that met their inclusion criteria did not meet the inclusion criteria of the systematic review conducted for this guideline, predominantly because of the level of population contamination (i.e. mixture of patients with focal and generalised epilepsy types) was greater than the threshold set out in the review protocol. Hawkins and colleagues also used different discounting rates than those currently outlined in the NICE reference case. The study by Spackman and colleagues compared levetiracetam and zonisamide, each followed by lamotrigine, from the perspective of the Scottish NHS. Although there was nothing fundamentally wrong with the study, the limited number of comparators makes it impossible to draw definitive conclusions on all relevant AEDs. In addition to the limitations highlighted above, both studies had three potentially serious limitations that required original modelling be undertaken. First, both evaluations used drug costs that were current at the time they were undertaken, in 00-0 for Hawkins and colleagues and 00 for Spackman and colleagues. Since then, the unit costs for lamotrigine have reduced dramatically as it came off patent. This is less likely to affect the conclusions from Spackman, but it could affect those of Hawkins and colleagues. Second, both studies only looked at the efficacy outcome of greater than 0% reduction in seizure frequency without also including 00% seizure reduction (achievement of seizure freedom). Whilst greater than 0% reduction in seizure frequency is the most oft reported outcome in studies undertaken in a refractory population, many studies also report the proportion of patients achieving complete seizure control. Capturing this less common, but still meaningful, clinical outcome was considered an important aspect to our analysis. Page 0 of

31 Finally, several new AEDs have become licensed for use as adjunctive therapy in focal epilepsy and neither previous cost-utility analysis has evaluated them. These newer drugs include eslicarbazepine acetate, lacosamide, pregabalin and zonisamide. Because the GDG was interested in the cost-effectiveness of these drugs as well as determining how new costs for lamotrigine might change previous conclusions, AEDs used as adjunctive therapy in adults with refractory focal epilepsy was prioritised for original economic analysis. The decision modelling presented here was developed in close collaboration between the health economist, NCGC technical team and GDG members. Methods.. Model overview The analysis set out to evaluate the comparative cost-effectiveness of different AEDs used as adjunctive therapy in the treatment of adults with refractory focal epilepsy. This model follows the same general methods described previously for the monotherapy model in section.0., measuring health effects in terms of QALYs and considering costs from a UK NHS and PSS perspective. Costs and benefits of different treatment strategies were calculated over a -year time horizon for a hypothetical cohort of patients with refractory focal epilepsy. A random effects network meta-analysis using a hierarchical Bayesian model based on individual clinical trial results was performed to enable direct comparisons of AEDs (full description in appendix O). As in the model to evaluated monotherapy AED, the clinical trial data provided response and withdrawal estimates for AEDs in the short term, typically up to months. Open-label clinical trial data was used to estimate the longer-term rates of adjunctive drug effect and withdrawal. The model also accounted for epilepsy related mortality linked to whether patients were seizure-free or not seizure-free. Costs and benefits were discounted at a rate of.% per annum. The performance of alternative treatment sequences was estimated using incremental cost-effectiveness ratios (ICERs), defined as the added cost of a given strategy divided by its added benefit compared with the next most expensive strategy. A threshold of 0,000 per QALY gained was used to assess cost-effectiveness. To assess the effect of uncertainty and imprecision around model inputs, a probabilistic analysis was undertaken... Model structure The structure of the decision-analytic model developed for this analysis was adapted from the model developed by Hawkins and colleagues and follows the latter half of that previously outlined in section.0.. In other words, it assumes that patients enter the adjunctive therapy model after having not achieved seizure control with or having withdrawn due to adverse events from monotherapy. Patients who start adjunctive therapy experience one of four outcomes: achieve seizure freedom; experience a reduction in seizure frequency of between 0% and %; experience a reduction of less than 0% (no response); or withdraw due to adverse events. Again, patients who respond either as seizure free or a 0% to % reduction in seizure frequency are assumed to continue adjunctive drug treatment for subsequent cycles. At this point, patients who are still refractory (non-responders and those withdrawing due to adverse events) are assumed to be maintained on monotherapy with an older AED. This was a simplification of reality, as these patients would normally be considered for further therapy or surgery. Page of

32 0 A multistate Markov model was created using TreeAge Pro 00 to capture the different costs and effects associated with a given drug treatment. It was built to reflect transitions between a set of mutually exclusive health states, defined by the aforementioned outcomes of treatment. The Markov model and how patients move through the pathway is illustrated in figure. Figure : Markov model of AED treatment The consequences of a given drug treatment are reflected as a set of possible transitions between health states over a series of discrete time periods, called cycles. In figure, health states are depicted as ovals and interventions are depicted as rectangles. Movement between the various health states is governed by transition probabilities which are derived from the systematic review of clinical effectiveness data and from observational and openlabel clinical trial data. Thirty -month cycles were modelled, resulting in a -year time horizon for the analysis. The probability of a patient responding to a given drug and remaining on treatment after the first cycle was derived from the drug-specific clinical trial data synthesised in the network meta-analysis. The probability that a patient remained on that treatment in subsequent cycles was based on observational data that was not specific to the drug being considered. During any cycle, patients can move from any health state to death, the probability of which is determined by age and whether or not they are seizure free... Model comparators Based on the indications listed in the BNF, March 00, drugs licensed for adjunctive therapy for adults with focal epilepsy are eslicarbazepine acetate (ESL), gabapentin (GBP), lacosamide (LCS), lamotrigine (LTG), levetiracetam (LEV), oxcarbazepine (OXC), pregabalin (PGB), tiagabine (TGB), topiramate (TPM), vigabatrin (VGB) and zonisamide (ZNM). Clinical trial data on efficacy and tolerability was available for all of the above drugs. Although there was also data for adjunctive sodium valproate and carbamazepine, they were not included in the analysis as they are most commonly prescribed as first-line AEDs. Therefore the analysis assumes that most patients will be combining an adjunctive drug to one of Page of

33 0 0 0 these and not the other way around. Phenytoin was also excluded from the analysis owing to its narrow therapeutic window... Treatment Effectiveness... Estimation of response to and withdrawal from adjunctive therapy during the first cycle The probabilities of treatment success during the first cycle of treatment with an adjunctive AED were estimated from the clinical trial data specific to a given drug. For adjunctive therapy, response was most commonly defined in the clinical trials as the achievement of a greater than 0% reduction in seizure frequency. This outcome was subsequently divided into patients achieving seizure freedom (00% seizure reduction) and patients achieving 0- % reduction in seizure frequency. Treatment failure was defined as either withdrawal caused by adverse events or a less than 0% reduction in seizure frequency. Patients that respond to adjunctive therapy in the first cycle are assumed to continue adjunctive therapy in subsequent cycles. The first probability hypothetical patients faced in the model was that of withdrawing due to adverse events (pwdae). To calculate pwdae for each individual AED, the odds ratio of a given AED compared to placebo from the network meta-analysis of adjunctive AEDs was converted into a relative risk and applied to the absolute probability of withdrawal due to adverse events for placebo using the following formula: pwdae = placebo risk relative risk Where: Re lative risk = odds ratio ( placebo risk ( odds ratio) ) 0 The absolute probability of withdrawal due to adverse events for placebo was calculated as part of the network meta-analysis presented in appendix O, and equals 0.00 (% CI 0.0 to 0.0). For the probabilistic analysis, uncertainty in the risk parameter for placebo was incorporated using a beta distribution and uncertainty in the comparative withdrawal estimates is incorporated by using 0,000 of the simulated odds ratios from the network meta-analysis. Using the simulated outputs allows us to preserve the joint posterior distribution from the network meta-analysis and any correlation of treatment effects. Hypothetical patients who did not withdraw due to adverse events next face a probability of response (>0% reduction in seizure frequency). To calculate the absolute probability of response to AED treatment, the same formulae were used to convert the odds ratio of a given AED compared to placebo from the network meta-analysis into a relative risk and applied to the absolute probability of response with placebo. The absolute probability of response for placebo was calculated as part of the network meta-analysis presented in appendix O, and equals 0. (% CI 0. to 0.0). Because these response figures were based on intention to treat calculations, and they were being implemented in the model as Page of

34 0 conditional upon having not withdrawn due to adverse events, they were converted into conditional probabilities using the following method: Probabitlity ( response not withdrawn) = relative risk placebo risk ( pwdae) For the probabilistic analysis, uncertainty in the risk parameter for placebo was incorporated using a beta distribution and uncertainty in the comparative response estimates is incorporated by using 0,000 of the simulated odds ratios from the network meta-analysis. During probabilistic implementation of the model, it was rare but possible that the calculation of this conditional probability could return a value greater than one. This would happen if the probability of achieving a greater than 0% reduction in seizure frequency was greater than the probability of not withdrawing due to adverse events. On the rare occasions when this happened it was assumed that 00% of patients not withdrawing due to adverse events experienced a greater than 0% reduction in seizure frequency. In the clinical trials, some proportion of patients that were counted as having achieved a greater than 0% reduction in seizure frequency had actually experienced a 00% reduction in their seizure frequency. In order to capture this outcome in the model, probabilities of achieving seizure freedom conditional upon having achieved at least a 0% reduction in seizure frequency and not having withdrawn due to adverse events were calculated from all trials including the efficacy outcome of greater than 0% reduction in seizure frequency. In the base case, this proportion was derived by summing events (achievement of seizure freedom) and populations across trial arms of a given drug and assuming zero events occurred where none were reported. For example, if four studies that included drug A measured the outcome of greater than 0% reduction in seizure freedom and two of them also reported some number of patients achieving seizure freedom, then the number of patients achieving seizure freedom were summed across the two trials and divided by the sum of the population across all four trial arms. In a sensitivity analysis, proportions achieving seizure freedom were calculated using only trials that reported both efficacy outcomes. Uncertainty around these conditional probabilities was incorporated using beta distributions. The median odds ratios for withdrawal due to adverse events and achievement of a greater than 0% reduction in seizure frequency from the network meta-analysis are summarised in table and the resulting drug-specific probabilities of withdrawal and conditional probabilities of each category of response are presented in table 0. Table : Relative effects from network meta-analysis for adjunctive AEDs compared to placebo in refractory focal epilepsy Drug Withdrawal due to AE Response (>0% reduction) Median odds ratio Relative risk Median odds ratio Relative risk Lamotrigine.... Vigabatrin.... Gabapentin..0.. Levetiracetam Page of

35 0 Topiramate..0.. Oxcarbazepine Pregabalin.... Lacosamide Eslicarbazepine acetate Zonisamide.... Tiagabine.... Table 0: Absolute probabilities AEDs as adjunctive therapy in refractory focal epilepsy p>0% reduction not psf p>0% Drug pwdae withdrawn reduction Placebo Lamotrigine Vigabatrin Gabapentin Levetiracetam Topiramate Oxcarbazepine Pregabalin Lacosamide Eslicarbazepine acetate Zonisamide Tiagabine Estimation of response to adjunctive therapy in subsequent cycles Observational data and clinical experience of the GDG indicated that as for monotherapy, the probability of a patient changing treatment with adjunctive therapy decreases as the time they have been on a given treatment increases. The probabilities of treatment failure subsequent to the first cycle were interpolated from a published graph presented in Hawkins 00 based on the results of a tiagabine open-label follow-up study. The - month discontinuation probabilities and their distribution parameters are presented as part of the monotherapy model in section Cost Data Only direct NHS costs were considered, including costs of starting and switching therapies, additional healthcare costs required for the treatment of seizure free and not seizure free epilepsy patients and costs of specific anti-epileptic drugs. The calculation of costs associated with treatment initiation and alteration as well as those associated with a patient s seizure status are described in detail in sections.0.. and.0.. of the monotherapy model. The costs of managing adverse events and side effects were also excluded from this analysis. Page of

36 Specific AED costs Differences in the cost of individual AEDs are likely to be a determining factor in the total costs of each treatment option, and this parameter is driven by the assumption of dose used. Carbamazepine was assumed to be the baseline concomitant drug, and therefore used to cost the monotherapy (placebo) model arm. Adjunctive drug costs were calculated by estimating the average daily dose in milligrams and multiplying it by a weighted mean unit cost per milligram. Uncertainty in drug doses was incorporated using a gamma distribution with parameters based on the minimum and maximum recommended doses provided in the BNF v, September 00. A weighted mean, based on relative usage data from the Prescription Cost Analysis and unit costs from the BNF, was calculated for the unit cost per milligram of each individual AED vi,vii. For details on the weights applied and the calculation of the mean cost per milligram, see appendix Q. Daily doses, weighted mean unit costs, and total drug costs per -month cycle for each modelled AED are presented in table. Drug Table : Costs of individual adjunctive AEDs Recommended min dose (mg/day) Recommended max dose (mg/day) Cost/mg ( ) Implied mean dose (mg/day) Manually adjusted SE Gamma parameters Alpha Beta Mean - month cost ( ) CBZ a LTG LEV TPM OXC GBP ESL PGB b 0.. LCS TGB VGB ZNM a CBZ is assumed to be the monotherapy, concomitant AED with all adjunctive drugs and the generic older AED used as maintenance therapy b PGB unit cost is based on the cost per milligram of a 0 mg capsule. v This method was not used to estimate the dose of levetiracetam or pregabalin, as it would result in an overestimation of the mean daily doses. For levetiracetam, we used a mean daily dose of mg, reported by UCB Pharma during consultation and agreed by the GDG. For pregabalin, we used the minimum and maximum recommended in the SPC, as suggested by Pfizer Limited during consultation. vi This exact costing method could not be used for topiramate, because the cost for non-proprietary topiramate was available only on the NHS Electronic Drug Tariff (accessed 0 October 00). Therefore, the weighted average unit cost of topiramate was calculated using data from the Prescription Cost Analysis, the BNF (for branded Topamax) and the NHS Electronic Drug Tariff (for non-proprietary topiramate). vii This costing method was not used for pregabalin, because all capsules, regardless of size, are the same price (.). Estimating a weighted average unit cost based on the cost per milligram of different sized capsules would therefore be inappropriate. Instead, the unit cost is based on the cost per milligram of a 0 mg capsule only. Page of

37 Utilities (health-related quality of life) The QALYs gained for patients in each health state were calculated based on the utility estimates associated with being seizure free, having a 0-% reduction in seizure frequency or not responding to therapy (i.e. continuing to have uncontrolled seizures). Uncertainty in these estimates was incorporated using a gamma distribution parameterised as a function of the mean and standard deviation of these estimates. The utility weights presented in the monotherapy model were also used in the adjunctive model. For a discussion on how utility weights were identified and incorporated into the model, see section Mortality Patients who achieved seizure freedom with treatment faced the age-dependent death rate associated with experiencing no seizures. Patients who experienced a 0-% reduction or no response to treatment faced age-dependent death rates associated with experiencing seizures. These rates as well as how they were derived are described in detail in the methods of the monotherapy model (section.0.)... Sensitivity analysis In addition to the probabilistic analysis run to take account of uncertainty around the input parameters, various other sensitivity analyses, where one or more inputs were varied, were undertaken to test how sensitive conclusions of the analysis are to model assumptions and data sources. First, a scenario analysis in which lamotrigine and oxcarbazepine were removed was undertaken to identify the most cost-effective strategy for individuals who may have already tried these drugs as first-line monotherapy. The hypothetical cohort modelled in the base case was 0 years old at initiation of therapy. The GDG felt that although this is not an unreasonable age on which to base decisions, many people diagnosed with focal epilepsy will start AED therapy at a much younger age and may well reach the stage of requiring adjunctive treatment earlier in life. The model was therefore run for a younger hypothetical patient group, starting at age 0 years, in order to see how age might influence the cost-effectiveness of different AEDs. A -year time horizon was used in the base case, but the GDG recognised that many patients could be on therapy for shorter or longer periods. Therefore, in a series of sensitivity analyses, the model was run for 0 cycles ( years) and 0 cycles (0 years). Results Results of the basecase analysis and various other scenarios are detailed in the following sections. All results are means from the probabilistic analysis unless otherwise specified... Basecase Analysis Results of the basecase incremental analysis are presented in table in order of increasing total cost per patient. The health gain to individuals is presented in terms of total QALYs for each alternative AED as well. Page of

38 0 0 Table : Incremental analysis of basecase results Total cost Incremental Total Incremental ICER Drug ( ) cost ( ) QALYs QALYs ( /QALY) Placebo,. GBP,0. 0.0,000 LTG,. 0.0,0 (ext dom) VGB,.0 0.0,0 OXC, Dominated LEV 0, Dominated LCS 0,,. -0. Dominated TPM 0,, Dominated PGB,0, Dominated TGB,, Dominated ESL,, Dominated ZNM,, Dominated Continued monotherapy (placebo) has the lowest mean total cost (,) and also the lowest total benefit (. QALYs). Lamotrigine is ruled out through extended dominance by vigabatrin, which generates the most QALYs, and at a willingness to pay threshold of 0,000 per QALY appears to be the most cost-effective adjunctive AED in the analysis. Lacosamide is the least effective adjunctive AED and is dominated by gabapentin, lamotrigine, levetiracetam, oxcarbazepine and vigabatrin. Tiagabine, eslicarbazepine acetate and zonisamide are all dominated by lamotrigine, levetiracetam and oxcarbazepine; topiramate and pregabalin are dominated by levetiracetam and oxcarbazepine. These results are presented graphically, on the cost-effectiveness plane, in figure. Page of

39 0 Figure : Basecase results on the cost-effectiveness plane 0 The results of this analysis demonstrate that vigabatrin is a very effective AED. However, a major limitation of the analysis is that it does not account for the possible development of visual field defects that has been observed in patients taking vigabatrin over the long term. The model does not account for the additional costs of monitoring required whilst taking vigabatrin, nor does it account for the likely negative impacts on health-related quality of life associated with irreversible visual impairment. Therefore, although it is a drug with demonstrable effect in bringing about seizure control, it is discounted from the rest of the analysis owing to its toxic side effects. The new incremental results after excluding vigabatrin are presented in the cost-effectiveness plane in figure. Page of

40 0 Figure : Basecase results after excluding vigabatrin on the cost-effectiveness plane 0 A breakdown of total costs and QALYs is presented in table. Based on this breakdown, the largest single component of total costs for the treatment strategies is the cost of the drugs themselves. Drug costs (monotherapy, adjunctive therapy and maintenance therapy) make up between 0% and 0% of the total cost for the three least costly strategies (monotherapy, gabapentin and lamotrigine) and more than half for all other strategies. The more expensive the strategy is overall, the greater the proportion of the total cost is made up of drug costs. % of total cost for oxcarbazepine is made up of the cost of drugs alone, whereas % of the total costs of eslicarbazepine acetate and zonisamide are made up of drug costs. Outpatient costs represent the next largest component of total cost for all strategies. Table : Breakdown of basecase results (excluding VGB) Placeb o GBP LTG OXC LCS TGB LEV TPM ESL ZNM PGB QALYs Life years Undiscounted life years Total costs,,0,, 0,, 0, 0,,,,0 Drug costs Inpatient costs Outpatient costs Primary care costs 0 A&E costs Page 0 of

41 0 0 The probabilistic results of the base case analysis are presented in table, with % credible intervals around mean cost and QALY estimates and net benefit calculations and probabilities of each AED being optimal at thresholds of 0,000 and 0,000 per QALY. Table : Summary of basecase probabilistic results (excluding VGB) Net benefit (probability Net benefit (probability AED is most costeffective) AED is most cost- Mean total cost Mean total (threshold = effective) (threshold = AED (% CI) ( ) QALYs (%CI) 0K per QALY) 0K per QALY) Placebo,. ( to 0) (. to.), (.%),0 (0.%) GBP,0. ( to 0) (. to.), (0.%), (.%) LTG,.0 ( to 0) (. to.), (.%),0 (.%) OXC,. ( to ) (.0 to.), (.%), (.0%) LEV 0,. ( to ) (.0 to.), (.%), (.%) LCS 0,. ( to ) (. to.),000 (0%), (0%) TPM 0,. ( to 0) (. to.), (.%), (.%) PGB,0. ( to ) (. to.), (.%), (.%) TGB,. (0 to ) (. to.0), (0.0%) 0,0 (0.0%) ESL,.0 ( to ) (. to.), (0%), (0%) ZNM,. ( to ) (. to.), (0%), (0%) The probabilistic basecase results show that lamotrigine has the greatest probability of being cost-effective and oxcarbazepine has the second greatest at a 0,000 per QALY threshold. At a 0,000 per QALY threshold, lamotrigine and oxcarbazepine have virtually the same probability of being optimal, with the remaining likelihood split fairly evenly between gabapentin, levetiracetam, topiramate and pregabalin.. Based on these estimates, there is a high degree of uncertainty between oxcarbazepine and lamotrigine as to which is the most cost-effective adjunctive AED. In the base case, drugs such as lacosamide, tiagabine,, eslicarbazepine acetate and zonisamide have a less than % probability of being optimal at a threshold of 0,000 per QALY. Page of

42 Sensitivity analyses... Exclusion of lamotrigine and oxcarbazepine assuming they have been tried previously Based on the evidence reviewed for the treatment of newly diagnosed focal epilepsy, lamotrigine and oxcarbazepine were recommended as possible first-line AEDs. It is unlikely that lamotrigine or oxcarbazepine would be considered a possible adjunctive treatment in patients who had previously tried it as monotherapy and who had not achieved seizure control or had experienced intolerable adverse events. The results of this scenario are presented in table and figure. When lamotrigine and oxcarbazepine are removed from the list of therapy options, levetiracetam is the most effective adjunctive AED, but at a willingness to pay of 0,000 per QALY, its additional benefit over gabapentin may not justify the additional cost. Its probability of being optimal is.% at 0,000 per QALY and.% at 0,000 per QALY. Gabapentin has a greater probability of being optimal at both thresholds:.% at 0,000 per QALY and.% at 0,000 per QALY. Eslicarbazepine acetate, lacosamide, pregabalin, tiagabine, topiramate and zonisamide are dominated by levetiracetam in this scenario. Table : Incremental analysis of results where LTG and OXC are excluded Drug Total cost ( ) Incremental cost ( ) Total QALYs Incremental QALYs ICER ( /QALY) Probability most costeffective ( 0,000 per QALY) Placebo,..0% GBP,0. 0.0,000.% LEV 0,,. 0.0,.% LCS 0, Dominated 0% TPM 0, Dominated.% PGB, Dominated.% TGB, Dominated 0.% ESL,, Dominated 0% ZNM,, Dominated 0% Page of

43 0 Figure : Results on the cost-effectiveness plane where lamotrigine and oxcarbazepine are excluded Page of

44 Alternative costing scenarios for newer AEDs In the base case we used a weighted average cost for both lamotrigine and topiramate, based on the relative prescribing of different sized tablets and proprietary and nonproprietary products reported in the Prescription Cost Analysis. In order to see how the results might change if only generic products were offered to patients, the model was rerun using costs of only generic lamotrigine and topiramate. The rank of lamotrigine did not change from the base case, but when its cost was based on the generic product only, then it extendedly dominated gabapentin and had an incremental cost-effectiveness ratio of, compared to placebo. When the cost of generic topiramate was used, it made topiramate the third least costly adjunctive AED overall, in contrast to the base case where it was sixth least costly. Topiramate, when compared to generic lamotrigine, is unlikely to be considered cost-effective, with an incremental cost-effectiveness ratio of,. However, when topiramate is compared to the weighted average cost of lamotrigine, the ICER comes down to,0. Potentially the best way to illustrate how these uncertainties impact the results is to compare the probabilities of each drug being most cost-effective under these different costing scenarios. These probabilities, at a willingness to pay threshold of 0,000 are summarised in table for six of the comparators. Table : Probability most cost-effective at 0,000 per QALY threshold using alternative costing scenarios Including st line AEDs Excluding st line AEDs AED Base case Generic TPM Generic LTG and TPM Base case GBP 0.%.%.%.0%.% LTG.%.%.% NA NA TPM.% 0.%.% %.% OXC.%.%.% NA NA LEV.%.0% 0.%.0%.% PGB.0%.%.%.%.0% As was the case in the analysis of first line AEDs, the GDG was interested in exploring how a change in the cost of levetiracetam might affect its cost-effectiveness compared to other AEDs. This was particularly important to explore given that levetiracetam has recently lost its patent protection and therefore generic products are expected to enter the market within months of this guideline s publication. However, given that generic levetiracetam is not yet on the market, no costs are available. We therefore performed a sensitivity analysis on levetiracetam s unit cost, assuming that it falls by 0%, 0% and 0%. This sensitivity analysis was performed within the analysis where costs for generic lamotrigine and topiramate were used. Results are presented in table as the probability of levetiracetam being the most cost-effective adjunctive AED and in terms of the incremental costeffectiveness of levetiracetam compared to lamotrigine (when included) and topiramate, when lamotrigine was excluded. Table : Results of sensitivity analysis on unit cost of levetiracetam Scenario Incremental costeffectiveness ratio ( /QALY) Probability LEV is most cost-effective at 0,000 threshold Page of

45 0 0 0 Including st lines LEV compared to LTG Generic LTG and TPM 0,.% 0% reduction in cost,0.% 0% reduction in cost,.% 0% reduction in cost dominates.% Excluding st lines LEV compared to TPM Generic LTG and TPM,.% 0% reduction in cost, 0.% 0% reduction in cost 0,.% 0% reduction in cost dominates 0.% This sensitivity analysis shows that although levetiracetam at its current costs is unlikely to be considered cost-effective compared to either generic lamotrigine or topiramate, it s cost effectiveness improves substantially at a 0% and 0% reduction in unit cost. When lamotrigine and oxcarbazepine are removed from the analysis because they have been trialled first as monotherapy, then a 0% reduction in the unit cost of levetiracetam brings its incremental cost-effectiveness down to, from, compared to topiramate. If the cost reduces by 0% to 0%, levetiracetam is likely to be cost-effective whether lamotrigine or oxcarbazepine are included or excluded from consideration. It is worth noting that this sensitivity analysis errs on the more conservative side given that generic costs for both lamotrigine and topiramate are used. This means that the difference in cost between either generic drug and levetiracetam is wider than in the base case and therefore the additional QALY gain must be that much larger to be considered costeffective.... Alternative probabilities of achieving seizure freedom In the base case, probabilities of achieving seizure freedom for different adjunctive drugs were calculated using data from all trials reporting on the outcome achievement of a greater than 0% reduction in seizure frequency. Where the authors of the study did not report whether any of these responders had achieved a 00% seizure reduction, zero was assumed. In this sensitivity analysis, we took data only from trials that reported on both outcomes. For most drugs, this resulted in a greater proportion of >0% responders being classified as achieving seizure freedom. The results of the analysis using these alternative estimates are presented in table after removing AEDs that were dominated in both the basecase and sensitivity analysis. Table : Incremental analysis of results using alternative seizure free response probabilities Total cost Incremental Total Incremental ICER Drug ( ) cost ( ) QALYs QALYs ( /QALY) Placebo,. GBP, , LTG, , TPM 0,.00, , Page of

46 0 0 0 * Eslicarbazepine acetate, lacosamide, levetiracetam, oxcarbazepine, pregabalin, tiagabine and zonisamide were all dominated by lamotrigine, and thus excluded from the incremental analysis. When these alternative estimates for seizure freedom are used, topiramate becomes the most effective AED, with an incremental cost-effectiveness ratio just over 0,000 per QALY gained compared to the next most effective option, lamotrigine. Using this data and a 0,000 per QALY threshold lamotrigine has the greatest probability of being most costeffective (.%), followed by gabapentin (.%) and topiramate (.%). But, if lamotrigine and oxcarbazepine were removed from the analysis because they had been tried previously levetiracetam is ruled out not by direct dominance, but through extended dominance, and topiramate s incremental cost-effectiveness, now compared to gabapentin, improves to, per QALY. In this case, gabapentin has the greatest probability of being optimal (0.%) followed by topiramate (.%) and pregabalin (.%).... Additional scenario analyses The model was rerun to reflect several different scenarios, each designed to test a particular assumption related to the time horizon and hypothetical cohort. In all scenarios, monotherapy (placebo) is the least cost and least effective strategy and gabapentin is consistently the least cost adjunctive AED. The effects of a shorter and longer time horizon as well as a younger starting age are summarised in table. The conclusions drawn from the base case regarding the costeffectiveness of lamotrigine do not appear sensitive to these changes. However, the costeffectiveness of oxcarbazepine does appear sensitive to alternative assumptions, with its incremental cost-effectiveness over lamotrigine increasing substantially with a younger cohort and shorter time horizon. The same trend applies to the cost-effectiveness of levetiracetam when lamotrigine and oxcarbazepine are excluded on the ground that they may have been tried first as monotherapy. However, when longer time horizon is used, a scenario that may more accurately reflect reality, the cost-effectiveness of each drug actually improves relative to the base case. Table : Impact of different scenarios on ICERs of LTG, OXC and LEV Scenario LTG (compared to GBP) ( /QALY) OXC (compared to LTG) ( /QALY) LEV (compared to GBP when LTG and OXC excluded) ( /QALY) Base Case,0,000, Cohort starting age = 0 years, 00,, Time horizon = years,,, Time horizon = 0 years,,,0 Cohort starting age = 0 years and time horizon = years,,, 0 Discussion The economic evidence for adjunctive therapy used to treat adults with refractory focal epilepsy came from two studies,, both of which had limitations preventing their direct application to the guideline decision context. Both used out of date costs and neither Page of

47 included all the comparators considered relevant to the guideline. In addition, neither analysis took account of the possibility that patients could become seizure free on adjunctive therapy, an outcome that has significant impacts on quality of life and NHS resource use. Therefore, the aim of this analysis was to fill in the gaps left in the published literature by evaluating the relative cost-effectiveness of different licensed AEDs used in the adjunctive treatment of adults with refractory focal epilepsy. The base case analysis and all sensitivity analyses demonstrate the effectiveness and costeffectiveness adjunctive lamotrigine as an optimal drug to try in this group of patients, assuming it has not already been used as monotherapy. Adjunctive gabapentin was less costly and less effective than lamotrigine, but may also be a reasonable option to offer patients with epilepsy that has been refractory to previous treatments. In all but one sensitivity analysis, oxcarbazepine was shown to be the most effective adjunctive therapy and expected values indicate that its additional cost compared to its additional benefit are over the conventional 0,000 per QALY threshold. However, its likelihood of being the most cost-effective AED increased from % of simulations to % of simulations when the threshold willingness to pay was increased to 0,000 per QALY. Its relative costeffectiveness also improves when the modelled time horizon is lengthened. In the one sensitivity analysis where it was not most effective, alternative probabilities of achieving seizure freedom were used to reflect variation in the reporting of trial outcomes. The GDG considered that the base case probabilities for 00% seizure reduction are likely to be more reasonable given that study investigators would most likely have reported it if it had indeed been observed. Because the GDG made the recommendation for lamotrigine and oxcarbazepine as first line monotherapy, it was important to identify the most cost-effective drugs for use as adjunctive therapy if lamotrigine and oxcarbazepine had already been tried and failed to produce the desired response. In this scenario, gabapentin emerged as the drug with the greatest probability of being cost-effective at a willingness to pay threshold of 0,000 per QALY. However, levetiracetam and topiramate may also represent cost-effective options. Levetiracetam had an incremental cost-effectiveness ratio of, compared to gabapentin in this scenario, and this can only be assumed to decrease (thus becoming more cost-effective) if unit costs of levetiracetam come down by almost any amount upon introduction of generic competition. Although levetiracetam dominates topiramate in the base case, when the cost of topiramate is assumed to be only that of generic products, it becomes less costly than levetiracetam and has a greater probability of being cost-effective because levetiracetam s comparative cost-effectiveness is, per QALY well over the conventional threshold. However, if the cost of levetiracetam comes down, it is likely to be cost-effective compared to topiramate, with an ICER of, at a 0% reduction and 0, at a 0% reduction. In developing recommendations for adjunctive treatment of refractory focal epilepsy, the GDG considered the lack of certainty around the most cost-effective adjunctive AED. They ultimately chose to recommend lamotrigine and oxcarbazepine on the basis that they were the two drugs with the greatest probability of being cost-effective in the base case and other scenarios where they were included as possible treatment options. The GDG also recommended gabapentin on the basis that it emerged as the most likely cost-effective AED Page of

48 0 0 when lamotrigine and oxcarbazepine were not considered relevant treatment options. Given the uncertainties driving the results of the other sensitivity analyses, particularly around assumptions of cost, the GDG decided to recommend topiramate and levetiracetam as additional adjunctive AEDs. The GDG considered that the weighted average cost of topiramate in the base case was relatively high due to the fact that generic products have only recently come to market and therefore the Prescription Cost Analysis may overestimate the relative use of branded Topamax. The GDG expects that use of generic topiramate will ultimately drive down the weighted average price. Similarly, they believe that the introduction and uptake of generic levetiracetam products (when available) will drive down the average price of the drugs and make levetiracetam more cost-effective. The sensitivity analysis showed that the unit cost of levetiracetam must only come down by 0% in order to be cost-effective when lamotrigine and oxcarbazepine are inappropriate options. The economic evidence for the other drugs included in the analysis eslicarbazepine acetate, lacosamide, pregabalin, tiagabine, zonisamide showed them to be more costly and less effective than other cost-effective treatment alternatives. On this basis, the GDG felt that they should not be recommended among initial adjunctive therapy options. Rather they should be considered only after more effective and cost-effective adjunctive treatments have failed to bring about the desired level of seizure control Limitations The analysis presented here has several limitations, many of which are the same as those listed in the evaluation of monotherapy. The GDG took these limitations into consideration as they used the results to develop guideline recommendations. Firstly, the analysis did not take account of any specific adverse events associated with particular drugs. However the GDG felt the model was still useful for decision-making as most adverse events can either be mitigated through dose adjustment or will disappear once the drug is withdrawn. The data used to model the drug-specific long-term effectiveness of AEDs used as adjunctive therapy was limited to a single open-label follow-up study of adjunctive tiagabine. Longterm data specific to individual drugs was not available and application of this tiagabine data was considered reasonable to reflect the decreasing trend of treatment failure with time. Finally, as in the analysis of monotherapy, the availability of utility data to inform the estimation of QALYs was quite limited. Further work in this area would be helpful for future cost-effectiveness modelling developed in the area of epilepsy. Conclusion Based on the clinical and health economic evidence, the GDG concluded that gabapentin, lamotrigine, levetiracetam, oxcarbazepine and topiramate were effective and cost-effective drugs to offer as initial adjunctive therapy in individuals with refractory focal epilepsy. Other drugs, such as eslicarbazepine acetate, lacosamide, pregabalin, tiagabine, zonisamide should be considered in the event that initial adjunctive therapies have failed to bring about an adequate level of seizure control or if patients experience intolerable side effects. Page of

49 0 0 0 Economic Evaluation of AEDs used as monotherapy and adjunctive therapy in the treatment of children with focal epilepsy Introduction The review of clinical evidence for AEDs used as monotherapy and adjunctive therapy in children with focal epilepsy showed broad similarity between different drug options, both in terms of efficacy and tolerability. This conclusion was made based upon the findings of the direct evidence. Unlike for the review of AEDs used as monotherapy in the treatment of adults with newly diagnosed focal epilepsy, no network of direct and indirect comparisons could be formed in the review for the treatment of children with which to undertake a network meta-analysis. And although there were several placebo-controlled trials in the adjunctive treatment of children, as there was only a single study available for each comparison, it was not considered necessary to synthesise the evidence as part of an indirect treatment comparison. Health economic evidence for AEDs used in the treatment of children with focal epilepsy was limited to one cost-utility analysis undertaken to inform previous NICE guidance (TA). The analysis, like the one presented here, was limited by a general lack of data on clinical outcomes, resource use and health state utilities. Due to the significant uncertainty in the model inputs, no strong conclusions about either cost-effectiveness or non-costeffectiveness of any drugs were drawn. Many of the limitations Frew and colleagues encountered still stand, but since they undertook their analysis, the cost of lamotrigine has reduced after coming off patent and new data regarding the efficacy and tolerability of adjunctive levetiracetam has become available. Because the GDG was interested in the relative cost-effectiveness of levetiracetam as well as determining how new costs for lamotrigine might change previous conclusions, AEDs used as monotherapy and adjunctive therapy in children with focal epilepsy was prioritised for original economic analysis. The decision modelling presented here was developed in close collaboration between the health economist, NCGC technical team and GDG members Model overview The analysis set out to evaluate the comparative cost-effectiveness of different AEDs used as monotherapy and adjunctive therapy in the treatment of children with focal epilepsy. This model follows the same general methods described previously for the monotherapy model in section.0., measuring health effects in terms of QALYs and considering costs from a UK NHS and PSS perspective. Costs and benefits of different treatment strategies were calculated over a -year time horizon for a hypothetical cohort of -year old children with newly diagnosed focal epilepsy (for monotherapy) and refractory focal epilepsy (for adjunctive therapy). Treatment effects were taken from the clinical evidence review summarised in chapter 0 and sections 0. and 0. of the guideline. As in the models designed to evaluate AED in the treatment of adults, the clinical trial data provided response and withdrawal estimates for AEDs in the short term, typically up to months. Open-label clinical trial data was used to estimate the longer-term rates of adjunctive drug effect and withdrawal. The model also accounted for epilepsy related mortality linked to whether Page of

50 patients were seizure-free or not seizure-free. Costs and benefits were discounted at a rate of.% per annum. The performance of alternative treatment sequences was estimated using incremental cost-effectiveness ratios (ICERs), defined as the added cost of a given strategy divided by its added benefit compared with the next most expensive strategy. A threshold of 0,000 per QALY gained was used to assess cost-effectiveness. To assess the effect of uncertainty and imprecision around model inputs, a probabilistic analysis was undertaken... Model structure Although the individual sampling model presented by Frew and colleagues had some advantages for the evaluation of AEDs used in the treatment of children, a different model structure was pursued here. The structure of the decision-analytic model developed for this analysis was adapted from the model developed by Hawkins and colleagues and is presented in full in section.0.. It was validated by the GDG as a reasonable reflection of current practice for adults and for children. For an illustration of the health states and how patients are assumed to move through them, please refer to figure in section Model comparators Based on the indications listed in the BNF, March 00, drugs licensed as monotherapy for focal epilepsy in children are carbamazepine (CBZ), lamotrigine (LTG), levetiracetam (LEV), oxcarbazepine (OXC), sodium valproate (VPA) and topiramate (TPM). Oxcarbazepine and topiramate are only licensed as monotherapy in children over years of age, lamotrigine in children over years and levetiracetam in children over years. Drugs licensed as adjunctive therapy for children with focal epilepsy are gabapentin (GBP), lamotrigine (LTG), levetiracetam (LEV), oxcarbazepine (OXC), tiagabine (TGB), topiramate (TPM) and vigabatrin (VGB). Adjunctive lamotrigine and topiramate are licensed for use in children over years. Gabapentin and oxcarbazepine are licensed for children over years and levetiracetam and tiagabine are only licensed as adjunctive therapy for children over years of age. The specific drugs included in the evaluation of monotherapy AEDs were determined by the availability of clinical trial data on outcomes of efficacy and tolerability. On that basis, only carbamazepine, lamotrigine and oxcarbazepine were included in the evaluation for monotherapy and gabapentin, lamotrigine, levetiracetam, oxcarbazepine and topiramate were included in the evaluation for adjunctive therapy... Treatment Effectiveness... Estimation of response to and withdrawal from monotherapy during the first cycle The probabilities of treatment success during the first cycle of treatment were estimated from the clinical trial data specific to a given drug. For monotherapy, response was defined as the achievement of seizure freedom without withdrawal of treatment. Treatment failure was defined as either withdrawal caused by adverse events or a less than 00% reduction in seizure frequency. For adjunctive therapy, response was defined as either the achievement Page 0 of

51 0 0 0 of seizure freedom or between a 0% and % reduction in seizure frequency, and failure was defined as either withdrawal caused by adverse events or a less than 0% reduction in seizure frequency. Patients that respond to therapy in that first cycle are assumed to continue therapy in subsequent cycles. For monotherapy, the probabilities of achieving each outcome were incorporated into the model using a Dirichlet distribution. Parameters of Dirichlet distributions for each drug evaluated as monotherapy are presented in table 0. Table 0: Dirichlet distribution parameters for AEDs used as monotherapy in the treatment of children with newly diagnosed focal epilepsy Seizure Withdrawn due to No Drug Study Total free AE response Nieto-Barrera CBZ 00 (.0%) (.%) (.%) LTG Nieto-Barrera 00 (.%) (.%) (.%) OXC Guerreiro (0.%) (.%) (.%) For adjunctive therapy, the probabilities for all outcomes were calculated using the same data and using the same method as outlined in section... for adults with refractory focal epilepsy. The GDG decided that efficacy data for children should remain separate from adults for the evaluation of drugs used to treat newly diagnosed epilepsy. However, they agreed with recent European Medicines Agency guidance that stated it may be reasonable to extrapolate results from efficacy trials of adjunctive therapy performed in adults to children, provided the dose is established. On this basis, the efficacy and tolerability data for adjunctive AEDs used to treat refractory focal epilepsy were combined for adults and children and used to inform the relevant parameters in this model. The first probability hypothetical patients faced in the model was that of withdrawing due to adverse events (pwdae). To calculate pwdae for each individual AED, the odds ratio of a given AED compared to placebo from the network meta-analysis of adjunctive AEDs was converted into a relative risk and applied to the absolute probability of withdrawal due to adverse events for placebo using the following formula: pwdae = placebo risk relative risk Where: Re lative risk = odds ratio ( placebo risk ( odds ratio) ) 0 The absolute probability of withdrawal due to adverse events for placebo was calculated as part of the network meta-analysis presented in appendix O, and equals 0.00 (% CI 0.0 to 0.0). For the probabilistic analysis, uncertainty in the risk parameter for placebo was incorporated using a beta distribution and uncertainty in the comparative withdrawal estimates is incorporated by using 0,000 of the simulated odds ratios from the network Page of

52 0 0 meta-analysis. Using the simulated outputs allows us to preserve the joint posterior distribution from the network meta-analysis and any correlation of treatment effects. Hypothetical patients who did not withdraw due to adverse events next face a probability of response (>0% reduction in seizure frequency). To calculate the absolute probability of response to AED treatment, the same formulae were used to convert the odds ratio of a given AED compared to placebo from the network meta-analysis into a relative risk and applied to the absolute probability of response with placebo. The absolute probability of response for placebo was calculated as part of the network meta-analysis presented in appendix O, and equals 0. (% CI 0. to 0.0). Because these response figures were based on intention to treat calculations, and they were being implemented in the model as conditional upon having not withdrawn due to adverse events, they were converted into conditional probabilities using the following method: Probabitlity ( response not withdrawn) = relative risk placebo risk ( pwdae) For the probabilistic analysis, uncertainty in the risk parameter for placebo was incorporated using a beta distribution and uncertainty in the comparative response estimates is incorporated by using 0,000 of the simulated odds ratios from the network meta-analysis. During probabilistic implementation of the model, it was rare but possible that the calculation of this conditional probability could return a value greater than one. This would happen if the probability of achieving a greater than 0% reduction in seizure frequency was greater than the probability of not withdrawing due to adverse events. On the rare occasions when this happened it was assumed that 00% of patients not withdrawing due to adverse events experienced a greater than 0% reduction in seizure frequency. In the clinical trials, some proportion of patients that were counted as having achieved a greater than 0% reduction in seizure frequency had actually experienced a 00% reduction in their seizure frequency. In order to capture this outcome in the model, probabilities of achieving seizure freedom conditional upon having achieved at least a 0% reduction in seizure frequency and not having withdrawn due to adverse events were calculated from all trials including the efficacy outcome of greater than 0% reduction in seizure frequency. In the base case, this proportion was derived by summing events (achievement of seizure freedom) and populations across trial arms of a given drug and assuming zero events occurred where none were reported. For example, if four studies that included drug A measured the outcome of greater than 0% reduction in seizure freedom and two of them also reported some number of patients achieving seizure freedom, then the number of patients achieving seizure freedom were summed across the two trials and divided by the sum of the population across all four trial arms. Uncertainty around these conditional probabilities was incorporated using beta distributions. The median odds ratios for withdrawal due to adverse events and achievement of a greater than 0% reduction in seizure frequency from the network meta-analysis are summarised in table and the resulting drug-specific probabilities of withdrawal and conditional probabilities of each category of response are presented in table. Page of

53 0 0 0 Table : Relative effects from network meta-analysis for adjunctive AEDs compared to placebo in refractory focal epilepsy Drug Withdrawal due to AE Response (>0% reduction) Median odds ratio Relative risk Median odds ratio Relative risk Lamotrigine.... Gabapentin..0.. Levetiracetam Topiramate..0.. Oxcarbazepine Table : Absolute probabilities AEDs as adjunctive therapy in refractory focal epilepsy p>0% reduction not psf p>0% Drug pwdae withdrawn reduction Placebo Lamotrigine Gabapentin Levetiracetam Topiramate Oxcarbazepine Estimation of response to treatment in subsequent cycles Observational data and clinical experience of the GDG indicated that for monotherapy and adjunctive therapy, the probability of a patient changing treatment decreases as the time they have been on a given treatment increases. The probabilities of treatment failure subsequent to the first cycle on monotherapy were derived from long-term NGPSE followup data. This data was used in a sensitivity analysis in the model to evaluation monotherapy AEDs for adults and is presented in section.0.. The probabilities of treatment failure subsequent to the first cycle for adjunctive therapy were interpolated from a published graph presented in Hawkins 00 based on the results of a tiagabine open-label follow-up study. The -month discontinuation probabilities and their distribution parameters are presented as part of the monotherapy model in section Cost Data Only direct NHS costs were considered, including costs associated with starting and switching therapies, additional healthcare costs required for the treatment of seizure free and not seizure free epilepsy patients and costs of specific anti-epileptic drugs. The same general methods were applied to the calculation of costs for children as were applied for Page of

54 adults. However, estimates of resource use and unit costs are different. The calculation of each cost component is described in detail in the following sections.... Initiating and switching to a new AED Previous published analyses have estimated that on average one GP visit and one outpatient visit would be required when a patient starts or switches treatment with AEDs. The GDG felt that this was a gross underestimation for the treatment of adults and children and agreed on alternative estimates based on their own clinical experience. Their estimates of healthcare resource use are summarised in table. Unit cost estimates applied to these estimates of resource use were taken from the PSSRU 0 and NHS reference costs. Table : Unit costs, health service use and total costs for starting and switching AED therapy Cost per Starting AED therapy Switching AED therapy Health Service use visit a Number Total Number Total ( ) of visits b cost ( ) of visits b cost ( ) GP Neurology outpatient initial visit Neurology outpatient follow-up Phone-call follow-up Total cost per patient 0 a Unit costs from PSSRU 0 and NHS reference costs b based on GDG consensus opinion... Healthcare costs associated with seizure status Individuals with epilepsy access healthcare services more than individuals without epilepsy, but how much and how often they access services has been linked to whether they are experiencing seizures or not. Therefore, one element of cost differences between treatments will be determined by their effectiveness in controlling seizures. Frew and colleagues solicited estimates of NHS resource use from clinical experts with experience providing care for children with epilepsy. They did not publish the estimates on GP consultations, outpatient attendances, visits to A&E, hospitalisations, or telephone calls to/from the family for advice. In the absence of their estimates to use, the GDG was consulted and asked their opinions of likely resource use. These estimates were then combined with probabilities of resource use derived from a retrospective study by Jacoby and colleagues. They conducted a retrospective study in on a cohort of, UK adults and children with active epilepsy. Patients were asked to describe the frequency of contacts they had with various health services over the last year. Patients were not asked about length of hospital stays for inpatient episodes or whether outpatient visits were new referrals or follow-up visits. The same approach to costing was taken in this analysis as was taken in the previously presented model to evaluate AEDs used in the treatment of adults. Probabilities of service use for children are taken from Jacoby and colleagues and multiplied by an expected number of contacts estimated by the GDG. In the study by Jacoby and colleagues, no Page of

55 children reported contact with inpatient or A&E services. As this may have been influenced by a small sample size, the probabilities of service use for adults were used to inform these parameters for children. The unit costs for inpatient stays, A&E episodes and outpatient attendances with paediatric specialists were each calculated as a weighted mean of many NHS reference cost components. Relative weights applied to each component were based on the activity level reported in the NHS reference cost schedule We assumed that the interquartile range for any given NHS reference cost fit a gamma distribution. Based on that assumption, we took the mean and manually adjusted the standard error estimate to calculate alpha and beta parameters for a gamma distribution that would come closest to reproducing the interquartile range reported in the NHS reference costs schedule. For the probabilistic analysis, each cost component was varied, multiplied by its relative weight and then summed with other cost components to equal the total unit cost for a given service. The unit cost of a GP consultation was taken from PSSRU 0 and is assumed to be constant. The probabilities of service use, expected number of contacts, unit costs of health services and calculated total costs are summarised in table. Table : Average yearly cost of seizures children Health service use Probability of use Seizure free a Not seizure free b Expected no of visits given non-zero use c Unit cost d Seizure free (=a*c*d) Cost ( ) Not seizure free (=b*c*d) Inpatient , A&E Outpatient (first visit) Outpatient (follow-up) 00 GP Expected total cost per child patient, a,b Annual probability of accessing a service dependent upon seizure status, from Jacoby and colleagues c Based on GDG consensus opinion d Unit costs from PSSRU 0 and NHS reference costs... Specific AED costs Differences in the cost of individual AEDs are likely to be a determining factor in the total costs of each treatment option, and this parameter is driven by the assumption of dose used. Recommended doses for the treatment of children are given milligrams per kilogram and are therefore expected to increase with age until adult doses are recommended. Mean weights for children up to the age of years were incorporated into the model using data from the Health Survey for England 00 and were used to calculate mean daily doses. Childhood weights were assumed to be normally distributed and were incorporated into the model using a normal distribution. Daily drug costs were calculated in the model by multiplying the mean weight of a child at any given age by the recommended daily dose in milligrams per kilogram and by a weighted Page of

56 0 0 0 mean unit cost per milligram. Uncertainty in drug doses was incorporated using a gamma distribution with parameters based on the minimum and maximum recommended doses provided in the BNF, March 00. A weighted mean, based on relative usage data from the Prescription Cost Analysis and unit costs from the BNF 00, was calculated for the unit cost per milligram of each individual AED. Daily doses with associated gamma distribution parameters and weighted mean unit costs for each modelled monotherapy AED are presented in table and each modelled adjunctive therapy in table. Table : Costs of individual AEDs used as monotherapy in children Drug Recommende d min dose (mg/day) Recommende d max dose (mg/day) Cost/mg ( ) Implied mean dose (mg/day) Manuall y adjuste d SE Gamma parameters CBZ ( to yrs) CBZ ( to 0 yrs) CBZ (0 to yrs) LTG mg/kg/day mg/kg/day mg/kg/day OXC Table : Costs of individual AEDs used as adjunctive therapy in children Drug (under yrs) Recommende d Minumum dose (mg/kg/day) Recommende d maximum dose (mg/kg/day) Cost/mg ( ) Implied mean (mg/kg/day ) Manuall y adjusted SE Alpha Beta Gamma parameters LTG (with CBZ) LEV TPM OXC GBP Once hypothetical patients reach the age of years, they are assumed to move to adult doses, the costs of which are presented in section.0.. for monotherapy and section... for adjunctive therapy. The cost of treatment in the maintenance phase of the model, for patients who have failed adjunctive treatment with comparator AEDs, is assumed to be equal to that of an older AED used as monotherapy. Because carbamazepine is the most commonly prescribed older AED for focal epilepsy, it was used as the maintenance drug in the model.... Exclusion of costs associated with adverse events or side effects The costs of adverse events are challenging to estimate because accurate estimates require both the incidence of adverse events and the treatment consequences. Because most adverse events can be assumed to disappear with either dose adjustment or withdrawal of treatment and can be expected to require no further treatment, they were not considered a priority area for inclusion in the model. The GDG considered the risk of possible adverse events qualitatively, but did not feel that the model was lacking for not having looked at specific adverse events. Alpha Beta Page of

57 0 0.. Utilities (health-related quality of life) The QALYs gained for patients in each health state were calculated based on the utility estimates associated with being seizure free, having a 0-% reduction in seizure frequency or not responding to therapy (i.e. continuing to have uncontrolled seizures). Uncertainty in these estimates was incorporated using a gamma distribution parameterised as a function of the mean and standard deviation of these estimates. No utility data for children has become available since Frew and colleagues undertook their analysis. The utility weights used in their model were based on responses from paediatric neurology experts, wherein they were asked to rank a set of six epilepsy-related health states and then classify each state using the EQ-D descriptor. Given that no new data was available, a subset of the health state utilities ranked and weighted by Frew and colleagues were used in our model. Uncertainty in the estimates was incorporated using a gamma distribution around the disutility estimates between health states. Mean utilities, disutilities and distribution parameters are presented in table. Table : Parameters used to estimate child health state utilities based on data Frew and colleagues Parameter description (parameter name) Value: mean (SE) Disutility: mean (SE) Gamma distribution parameters Alpha Beta Calculation Utility for a seizure free patient (U SF ) (0.0) (0.0) U SF Utility for a patient who has between 0-% reduction in seizure frequency (U 0-% reduction ) (0.) (0.) U SF -disutility 0-% reduction Utility for a patient who does not respond to therapy (U NR ) (0.) (0.) U 0-% reduction -disutility NR Utility associated with dead state (U dead ) 0 0 constant Mortality Children who achieved seizure freedom with treatment faced the age-dependent death rate associated with experiencing no seizures. Children who experienced a 0-% reduction or no response to treatment faced age-dependent death rates associated with experiencing seizures. These rates as well as how they were derived are described in detail in the methods of the monotherapy model (section.0.)... Sensitivity analysis In addition to the probabilistic analysis run to take account of uncertainty around the input parameters, various other sensitivity analyses, where one or more inputs were varied, were undertaken to test how sensitive conclusions of the analysis are to model assumptions and data sources. In the base case analysis comparing monotherapy AEDs, levetiracetam was used as the adjunctive drug all patients would move to if they did not respond to first-line treatment. In a sensitivity analysis, gabapentin, the AED with the lowest expected daily cost, was assumed to be the adjunctive drug. Page of

58 0 0 A sensitivity analysis on the source of the utility data was run, substituting the utility weights from the adult population in place of those estimated by the clinical professionals. Finally, a sensitivity analysis around the time horizon was run in order to see how shorter or longer treatment duration might affect the overall cost-effectiveness of evaluated AEDs. We also examined how the starting age of the hypothetical cohort could impact on the results. Results Results of the basecase analysis and various other scenarios are detailed in the following sections. All results are means from the probabilistic analysis unless otherwise specified... Basecase Analysis - Monotherapy Results of the basecase incremental analysis for monotherapy AEDs are presented in table in order of increasing total cost per patient. The health gain to individuals is presented in terms of total QALYs for each alternative AED as well. Table : Incremental analysis of monotherapy basecase results Total cost Incremental Total Incremental Drug ( ) cost ( ) QALYs QALYs ICER ( /QALY) CBZ,0 0. LTG, Dominated OXC, Dominated Carbamazepine has the lowest total cost and greatest total benefit. Based on the estimated means, treatment of all patients with carbamazepine dominates other AED therapies. These results are presented graphically, on the cost-effectiveness plane, in figure. 0 Figure : Monotherapy basecase results on the cost-effectiveness plane Page of

59 0 0 0 A breakdown of total costs and QALYs is presented in table. Based on the breakdown of costs, the largest single component of total costs for the different treatment strategies is the cost of outpatient attendances. The data from Jacoby and colleagues indicated the children were more likely to use outpatient services than adults, and this was generally in line with the experience of the GDG. Additionally, the unit cost of outpatient services for children was higher than for adults, as more appointments were expected to take place with consultant-level health professionals. The cost of any given drug makes up a much smaller proportion of total costs in children than in adults, likely because the doses are smaller. Table : Breakdown of monotherapy basecase results CBZ LTG OXC Total QALYs Life years... Undiscounted life years...0 Total Costs,0,0,0 Drug costs 0 0 Inpatient costs 0 Outpatient costs 0 0 Primary care costs A&E costs The probabilistic results of the base case analysis are presented in table 0, with % credible intervals around mean cost and QALY estimates and net benefit calculations with the probability of each AED being optimal at thresholds of 0,000 and 0,000 per QALY gained. Table 0: Summary of monotherapy basecase probabilistic results Net benefit (probability Net benefit (probability AED is most costeffective) AED is most cost- Mean total cost Mean total (threshold = effective) (threshold = AED (% CI) ( ) QALYs (%CI) 0K per QALY) 0K per QALY) CBZ,0 0. ( to ) (. to.), (.%), (.%) LTG,0 0. ( to ) (. to.), (.%), (.%) OXC (0 to ) (. to.), (.%),0 (.%) The probabilistic basecase results show that carbamazepine has the greatest probability of being cost-effective at both 0,000 and 0,000 per QALY thresholds. Based on these results, there is relatively little uncertainty in a decision to recommend carbamazepine as an initial treatment in children with newly diagnosed focal epilepsy. Page of

60 0 0.. Sensitivity analyses The model was rerun to reflect several different scenarios, each designed to test a particular assumption. The effect of these alternative assumptions on the base case results are summarised in table. Table : Impact of different scenarios on basecase ICERs Scenario CBZ LTG OXC Base case least cost dominated dominated Starting age 0 years,0 least cost dominated Adult utilities least cost dominated dominated GBP as adjunctive therapy least cost dominated dominated year time horizon least cost dominated dominated 0 year time horizon least cost dominated dominated In all but one scenario, carbamazepine remained the most effective and least costly AED. Only when the assumed starting age of the hypothetical cohort is older, that is 0 years of age, does lamotrigine become the least cost strategy. However, even in this scenario, carbamazepine is the most effective and is still cost-effective compared to lamotrigine, with an incremental cost-effectiveness ratio of,0 per QALY. Oxcarbazepine consistently has the greatest cost and the lowest overall benefit. 0.. Basecase Analysis Adjunctive therapy Results of the basecase incremental analysis for adjunctive therapy AEDs are presented in table in order of increasing total cost per patient. The health gain to individuals is presented in terms of total QALYs for each alternative AED as well. Table : Incremental analysis of adjunctive therapy basecase results Total Costs Incremental Total Incremental Drug ( ) Costs ( ) QALYs QALYs ICER ( /QALY) Placebo,.0 GBP,. 0.0 LTG,. 0.0, (ext dom) OXC,0. 0.0, TPM,. 0.00,0 (ext dom) LEV, ,00 Continued monotherapy (placebo) has the lowest total cost and lowest total benefit. Levetiracetam generates the most QALYs, but at a willingness to pay threshold of 0,000 per QALY appears unlikely to be cost-effective. Gabapentin, the least cost and least effective adjunctive AED, is very cost-effective compared to continued monotherapy. Page 0 of

61 0 Oxcarbazepine is potentially the most cost-effective adjunctive AED given our 0,000 per QALY threshold. Lamotrigine and topiramate are ruled out through extended dominance in this analysis. These results are presented graphically, on the cost-effectiveness plane, in figure 0. Figure 0: Adjunctive therapy basecase results on the cost-effectiveness plane 0 A breakdown of total costs and QALYs is presented in table. The breakdown of costs is consistent with those already presented for the results of monotherapy in children with newly diagnosed focal epilepsy where the largest single component of total costs for the different treatment strategies is the cost of outpatient attendances. Again, the cost of any given drug makes up a much smaller proportion of total costs in children than in adults, likely because the doses are smaller. Table : Breakdown of basecase results Adjunctive therapy Placebo LEV LTG TPM OXC GBP Total QALYs Life years Undiscounted life years Total Costs,,0,,,0, Drug costs 0 0 Inpatient costs 0 Outpatient costs Primary care costs A&E costs 0 0 Page of

62 0 0 0 The probabilistic results of the base case analysis are presented in table, with % credible intervals around mean cost and QALY estimates and net benefit calculations with the probability of each AED being optimal at thresholds of 0,000 and 0,000 per QALY gained. Table : Summary of basecase probabilistic results AED Placebo GBP LTG OXC TPM LEV Mean total cost (% CI) ( ), (0 to 0), ( to ), (0 to 0),0 (0 to 0), ( to ), ( to ) Mean total QALYs (%CI).0 (. to.0). (. to.0).0 (. to.0). (. to.0). (. to.0). (. to.0) Net benefit (probability AED is most cost-effective) (threshold = 0K per QALY) Net benefit (probability AED is most cost-effective) (threshold = 0K per QALY), (.%),0 (.%), (.%) 0, (.%), (.%), (0.%), (.%), (0.%),0 (.%),0 (.%), (.%), (.%) The probabilistic basecase results show that there is considerable uncertainty in determining which AED is likely to be the most cost-effective adjunctive therapy. At 0,000 per QALY, adjunctive gabapentin and oxcarbazepine are each the most costeffective strategy in a third of simulations. Continued monotherapy (placebo) and lamotrigine are each optimal in roughly % of simulations. Topiramate and levetiracetam are each most cost-effective in less than % of simulations. At a threshold of 0,000 per QALY, the decision is not much clearer. Gabapentin is optimal in 0% of simulations and oxcarbazepine is optimal in 0%.... Exclusion of lamotrigine and oxcarbazepine assuming they have been tried previously Based on the clinical and cost-effectiveness evidence for the treatment of adults with newly diagnosed focal epilepsy, lamotrigine and oxcarbazepine were recommended as possible first-line AEDs. Although this was not generally supported by the results of the economic analysis comparing carbamazepine, lamotrigine and oxcarbazepine in children with newly diagnosed focal epilepsy, the GDG considered the results of the analysis in adults to be more robust given the greater availability of data. Therefore, if lamotrigine or oxcarbazepine have been trialled as monotherapy, and either failed to bring about seizure freedom or was poorly tolerated, it is unlikely that they would subsequently be trialled as adjunctive treatment. Having removed lamotrigine and oxcarbazepine from consideration, the cost-effectiveness of placebo, gabapentin, levetiracetam and topiramate are presented in table and figure. Table : Incremental analysis of adjunctive therapy results where LTG and OXC are excluded Total cost Incremental Total Incremental ICER Probability most Drug ( ) cost ( ) QALYs QALYs ( /QALY) cost-effective Page of

63 0 ( 0,000 per QALY) Placebo,.0.% GBP, % TPM,, ,(ext dom).% LEV,0, ,0 0% Figure : Results without lamotrigine and oxcarbazepine on the cost-effectiveness plane When lamotrigine and oxcarbazepine are removed from the list of therapy options, levetiracetam is the most effective adjunctive AED, and is likely to be considered costeffective compared to gabapentin. Gabapentin has the greatest probability of being most cost-effective, but levetiracetam is most cost-effective in one-fifth of simulations. Topiramate is just barely extendedly dominated by levetiracetam, and is still most costeffective in % of simulations. 0.. Sensitivity analyses The model was rerun to reflect several different scenarios, each designed to test a particular assumption. The effect of these alternative assumptions on the base case results in terms of probability of being most cost-effective at a 0,000 per QALY threshold are summarised in table. Table : Impact of different scenarios on probabilities of being most cost-effective Adjunctive AED Probability most cost-effective at 0,000 per QALY Base Case starting age 0 years year time horizon 0 year time horizon Page of

64 0 0 0 Placebo.%.%.%.% GBP.%.%.%.% LTG.%.0%.%.% OXC.%.%.% 0.0% LEV.0%.%.0%.0% TPM.%.%.%.% These results indicate that continued monotherapy is more likely to be cost-effective if the hypothetical cohort is older at commencement of adjunctive therapy than in the base case. This is likely driven by the higher doses (and thus increased costs) required starting at age. Also of note from these sensitivity analyses is that adjunctive drug treatments become more cost-effective over shorter time horizons ( years) than longer (0 years). However, in general, these sensitivity analyses do not substantially alter any of the conclusions drawn from the base case. In a final sensitivity analysis, we explored the impact of different costing scenarios on the results of the base case. Table presents the base case results along with four alternative costing scenarios. First, the costs of non-proprietary lamotrigine and topiramate were used. Then, as was the case in both analyses undertaken for adults, the GDG felt it important to investigate the impact of reduced costs for levetiracetam given its recent patent expiry. Therefore, within the costing scenario for generic lamotrigine and topiramate, the cost of levetiracetam was assumed to drop by 0%, 0% and 0%. Table : Impact of different costing scenarios on probabilities of being most cost-effective Probability most cost-effective at 0,000 per QALY Adjunctive Base Case Generic Generic LTG & TPM and percent reduction in cost of LEV AED LTG & TPM 0 percent 0 percent 0 percent Placebo.%.%.0%.%.% GBP.% 0.% 0.0%.%.% LTG.%.%.%.%.% OXC.%.%.0% 0.%.0% LEV.0%.%.%.%.% TPM.%.%.%.%.% The results of these costing scenario analyses indicate that gabapentin is still the adjunctive AED most likely to be most cost-effective, unless the cost of levetiracetam comes down by 0 percent. Topiramate continues to be least likely to be most cost-effective, regardless of a significant cost reduction. Indeed, even when the cost of generic topiramate is used, it is still the second most costly adjunctive strategy and has an incremental cost-effectiveness ratio of,00 per QALY compared to oxcarbazepine. Lamotrigine, although not having particularly high probabilities of being most cost-effective among adjunctive strategies, is likely to be cost-effective compared to gabapentin, with an incremental cost-effectiveness ratio of approximately,000 in all scenarios. Page of

65 0 0 As for levetiracetam, when its cost reduces by only 0 percent, the results of the base case change very little. At this reduction its incremental cost-effectiveness ratio compared to oxcarbazepine only comes down from,00 in the base case to 0,00. However, when its unit cost is reduced by 0 percent, it becomes less costly and more effective than oxcarbazepine, with an acceptable incremental cost-effectiveness ratio of, compared to lamotrigine. Interestingly, at this reduction in cost, the results of the probabilistic analysis clearly illustrate the uncertainty in the decision, with the probability of being most cost-effective fairly evenly split between all drugs. At a 0 percent reduction in cost, levetiracetam dominates all strategies except for continued monotherapy (placebo) and adjunctive gabapentin, but very likely to be considered cost effective with an ICER of, compared to gabapentin and a % probability of being most cost-effective Discussion The economic evidence for AEDs as monotherapy in newly diagnosed focal epilepsy and as adjunctive therapy in refractory focal epilepsy in a children came from one cost-utility analysis undertaken to inform previous NICE guidance (TA). It had numerous limitations, rooted in a lack of available data. The analysis presented here cannot purport to overcome these limitations as many were still present during the development of this guideline. However, the aim of the analysis was to overcome some of the gaps in the evidence base and to inform evidence based decision-making in the best way possible. The base case analysis and all sensitivity analyses demonstrate the effectiveness and costeffectiveness of carbamazepine as the optimal drug to try first in children with newly diagnosed focal epilepsy. Lamotrigine and oxcarbazepine were more costly and less effective than carbamazepine in this analysis. In the analysis undertaken to evaluate the cost-effectiveness of adjunctive AEDs for the treatment of children with refractory focal epilepsy, there was considerable uncertainty in the results. Based on the mean cost and QALY estimates, oxcarbazepine is likely to represent the best value for money given a willingness to pay of 0,000 per QALY gained. Levetiracetam was the most effective AED, however its additional cost was not justified by its additional benefit compared to oxcarbazepine (ICER=,00). In a sensitivity analysis, however, its cost-effectiveness relative to other adjunctive AEDs improved substantially as the cost of levetiracetam decreases. Gabapentin, although the least cost and least effective adjunctive AED, is likely to be considered cost-effective, having the same probability of being most cost-effective as oxcarbazepine. Unlike in the analysis undertaken for adults with refractory focal epilepsy, lamotrigine does not emerge from this analysis as a likely cost-effective option, unless the cost of generic lamotrigine is assumed. Similarly, topiramate was not found to be as cost-effective in children with refractory focal epilepsy as among adults. The GDG interpreted this evidence in conjunction with the results of modelling for an adult population. The evidence in favour of carbamazepine in the treatment of children was in line with results for carbamazepine in an adult population. The GDG also thought that the clear cost-effectiveness of sodium valproate was likely to be true for children with focal Page of

66 epilepsy as well. And although the results for lamotrigine and oxcarbazepine showed them to be more costly and less effective, again the GDG looked to the evidence of costeffectiveness from SANAD wherein these two AEDs were shown to be cost-effective firstline drugs. As for AEDs used in the treatment of children with refractory focal epilepsy, the GDG considered the uncertainty around estimates of cost-effectiveness and decided that coupled with the evidence from the results of the model for adjunctive therapy for adults, lamotrigine and oxcarbazepine should be recommended as adjunctive therapy, particularly in those patients who have not yet tried them as monotherapy. The GDG considered that for the group of patients who have used lamotrigine or oxcarbazepine previously, the remaining AEDs, gabapentin, levetiracetam and topiramate could produce additional benefit at a reasonable cost to the NHS. Limitations Many of the limitations that Frew and colleagues faced could not be overcome in the undertaking of this updated analysis. Data on clinical effectiveness, resource use and health-related quality of life was still lacking and its impact on the results is evident in the wide credible intervals surrounding the costs and QALYs estimates. In addition to the lack of data, the analysis presented here suffers from the same limitations as the analysis developed for the treatment of adults. No specific adverse events associated with particular drugs were incorporate. However, the GDG felt that most side effects could be managed through dose adjustment or withdrawal of treatment. The long term effectiveness of different AEDs was based on very limited data and was not derived from a population of children. The GDG considered that the same trend towards a decreased probability of treatment failure as duration on therapy increases to be true to children as well as adults; therefore they did not feel this limitation to be a serious one. Conclusion The economic evaluation of carbamazepine, lamotrigine and oxcarbazepine showed carbamazepine to be the most cost-effective first line AED used to treat children with newly diagnosed focal epilepsy. This conclusion was insensitive to various changes in input parameters and alterations in structural assumptions. There was considerable uncertainty in the results of the economic evaluation of different AEDs used in the treatment of children who have failed first-line AEDs. No single AED could be identified as clearly cost-effective, although based on the expected costs and QALYs, gabapentin and oxcarbazepine are likely to be cost-effective. Sensitivity analyses around unit costs also indicated that lamotrigine and levetiracetam may be cost-effective if costs are reduced compared to the base case, by using the cost of generic lamotrigine and assuming some likely reduction in cost for levetiracetam. The GDG used evidence from the models developed for adults and children in order to make their recommendations on initial and subsequent treatments for individuals with focal epilepsy. Page of

67 Economic Evaluation of AEDs used as adjunctive therapy in the treatment of adults with refractory generalised tonic-clonic seizures Introduction The review of clinical evidence for AEDs used as monotherapy in adults with newly diagnosed generalised tonic-clonic seizures showed broad similarity between different drug options. Health economic evidence was extrapolated from a recent economic evaluation conducted alongside a UK randomised controlled trial comprised predominantly of patients with generalised epilepsy. Although evidence of cost-effectiveness from this trial was only available for sodium valproate, lamotrigine and topiramate, the clinical evidence also showed carbamazepine and oxcarbazepine to be effective drugs in this population. As these drugs had been shown to be cost-effective in other groups, the GDG was confident they would be cost-effective here as well. Therefore, the GDG recommended carbamazepine, lamotrigine, oxcarbazepine and sodium valproate for the first-line treatment of people experiencing newly diagnosed primary generalised tonic-clonic seizures. Topiramate was not recommended as a first-line drug option on the basis that, in the SANAD trial, it was considerably more expensive than sodium valproate and was less effective in terms of controlling seizures. There will be a population of patients who do not respond to monotherapy alone and who will require adjunctive therapy. This analysis aims to determine which drug this group of patients should be offered next. Although health economics is considered as part of the review for every clinical question, only certain questions are prioritised for original economic evaluation. Following the review of the clinical and cost-effectiveness literature it was considered that adjunctive therapy with lamotrigine, levetiracetam or topiramate was probably the appropriate option for treatment of adults with refractory generalised tonic-clonic seizures. However, given the relative size of this patient group and the limitations of published economic evidence assessing the cost-effectiveness of AEDs as adjunctive treatment in this group, the GDG identified this area as high priority for original economic analysis. The decision modelling presented here was developed in close collaboration between the health economist, NCGC technical team and GDG members. Methods.. Model overview The analysis set out to evaluate the comparative cost-effectiveness of different AEDs used as adjunctive therapy in the treatment of adults with refractory focal epilepsy. A cost-utility analysis was undertaken in line with the methods of the NICE reference case. QALYs were calculated using utility weights from EQ-D responses and UK public valuations. Costs were considered from a UK National Health Service and Personal Social Services perspective and expressed in 00-0 UK sterling. Healthcare costs associated with starting and switching therapies, additional healthcare costs required for the treatment of seizure free and not Page of

68 0 0 0 seizure free epilepsy patients and anti-epileptic drug costs were all included in the model. Costs of dealing with adverse events arising from treatment were not considered, as most will resolve when treatment is withdrawn. The cost-effectiveness analysis must be relevant for decision-making over the longer term, as most people with epilepsy can be expected to take AEDs for much of their lives. Therefore, the longer term costs and outcomes of treatment needed to be considered, including those associated with patients who do not respond to treatment. A year time horizon was considered sufficiently long enough to capture all relevant costs and benefits associated with competing drug treatments. In the basecase, the hypothetical cohort of patients was assumed to be 0 years old, but this was tested in a sensitivity analysis. The clinical trial data provided response and withdrawal estimates for AEDs in the short term, typically up to months. Open-label clinical trial data was used to estimate the longer-term rates of drug effect and withdrawal. The model also accounted for epilepsy related mortality linked to whether patients were seizure-free or not seizure-free. In order to scale future costs and health benefits to their present value, costs and benefits were discounted at a rate of.% per annum viii. The performance of alternative treatment sequences was estimated using incremental cost-effectiveness ratios (ICERs), defined as the added cost of a given strategy divided by its added benefit compared with the next most expensive strategy. A threshold of 0,000 per QALY gained was used to assess costeffectiveness. A probabilistic sensitivity analysis was undertaken to test the robustness of the results against the imprecision and uncertainty around input parameter point estimates (i.e. mean/median odds ratios, utility weights, etc). A probability distribution was defined for various model inputs and when the model is run, a value for each input was randomly selected from its specific probability distribution simultaneously and costs and QALYs were calculated using these random values. The model is run repeatedly in this case 0,000 times and results are summarised as mean costs and mean QALYs. Probability distributions in the analysis were based on error estimates from data sources, such as confidence intervals. 0.. Model structure The structure of the decision-analytic model developed for this analysis was adapted from models identified in the literature and included in the economic evidence review. It was validated by the GDG as a reasonable reflection of current practice. It assumes that all hypothetical patients entering the model have failed to respond to at least one previous AED used as monotherapy and are therefore still experiencing generalised tonic-clonic seizures. All hypothetical patients start adjunctive therapy and experience one of four viii Discounting is a technique used to reflect the present value of a cost or a health benefit that will occur at some future date. Because there is an opportunity cost to spending money now and there is a desire to experience health benefits now rather than in the future, discounting gives future costs and health benefits less weight compared to present costs and benefits. Page of

69 0 0 outcomes: achieve seizure freedom; experience a reduction in seizure frequency of between 0% and %; experience a reduction of less than 0% (no response); or withdraw due to adverse events. Patients who are still refractory (non-responders and those withdrawing due to adverse events) are assumed to be maintained on monotherapy with an older AED. This was a simplification of reality, as these patients would normally be considered for further therapy or surgery. A multistate Markov model was created using TreeAge Pro 00 to capture the different costs and effects associated with a given drug treatment. It was built to reflect transitions between a set of mutually exclusive health states, defined by the aforementioned outcomes of treatment. The Markov model and how patients move through the pathway is illustrated in figure. Figure : Markov model of AED treatment 0 The consequences of a given drug treatment are reflected as a set of possible transitions between health states over a series of discrete time periods, called cycles. Movement between the various health states is governed by transition probabilities which are derived from the systematic review of clinical effectiveness data and from observational and openlabel clinical trial data. Thirty -month cycles were modelled, resulting in a -year time horizon for the analysis. The probability of a patient responding to a given drug and remaining on treatment after the first cycle was derived from the drug-specific clinical trial data. The probability that a patient remained on that treatment in subsequent cycles was based on observational data that was not specific to the drug being considered. During any cycle, patients can move from any health state to death, the probability of which is determined by age and whether or not they are seizure free... Model comparators Based on the indications listed in the BNF, March 00, first choice drugs for primary generalised tonic-clonic seizures are carbamazepine (CBZ), lamotrigine (LTG) and sodium valproate (VPA). Second-line drugs include clobazam, levetiracetam (LEV), oxcarbazepine Page of

Appendix M Health Economic Evidence Extractions

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