Guidelines for Pharmacoeconomic Evaluations in Belgium

s and oral presentations usually provide insufficient information to assess the quality of their contents. They should be avoided as source for input values. For models that extrapolate to longer time periods, i.e. for chronic conditions or treatments/diseases with long term sequelae, it is recommended to present different scenarios to show the impact of different extrapolation approaches on the results (Drummond et al. Med Care 2005): • The first scenario assumes that the treatment effect disappears immediately in the extrapolated phase (stop-and-drop approach). This is the most conservative extrapolation approach. • The second scenario assumes that the incremental treatment effect stays the same as during the observed phase. • The third scenario assumes that the initial treatment effect fades out in the long term. The scenarios are all part of the reference case analysis because the choice of an extrapolation approach is mainly a judgement. By presenting different, sometimes extreme, scenarios, the uncertainty related to the effectiveness of the therapy in the extended period can be assessed. The presentation of scenarios is the most transparent option to show how robust the results are to the extrapolation approach used. Each scenario should be accompanied by appropriate sensitivity analyses on uncertain parameters as specified in guideline 11. 26 Pharmacoeconomic guidelines KCE reports 78 Models should be kept as simple as possible, but without omission of important processes. The original computer model should be put at the disposal of the Drug Reimbursement Committee upon request. Confidentiality will be guaranteed by the Committee. The choice of the modelling software is free. 4.11. 4 Calibration, face-validity and cross-validation of a model The results of the model should be logically consistent with real-life observations and data (calibration). For example, if age-specific incidences of a disease are used in a model, the total incidence generated by the model should not considerably be higher or lower than the observed incidence in the population, unless the difference can be explained by differences in the population structure. In other words, there must be a logical connection between inputs and outputs of a model. The results of the model should be intuitively correct, that is, the model should have face-validity. The model description should be transparent enough to allow an explanation of the differences with other models for the same interventions (crossvalidation). The presentation of the results of an economic model as a point estimate together with its appropriate uncertainty range is an absolute prerequisite. An economic model is by definition subject to uncertainty. The results are conditional upon the input data and the assumptions applied in the model. Both the uncertainty about the input data and the assumptions generate uncertainty in the outputs. This uncertainty should be appropriately presented, as the level of uncertainty might be an element in the decision making process. 4.12 GUIDELINE 11: HANDLING UNCERTAINTY AND TESTING ROBUSTNESS OF RESULTS Irrespective of the study design, the uncertainty surrounding the costeffectiveness/cost-utility estimates should be analysed using appropriate statistical techniques. Interval estimates should be presented for each uncertain parameter in the economic evaluation. The different aspects of uncertainty in the evaluation should be addressed, including methodological uncertainty and data uncertainty. For models, probabilistic sensitivity analyses should be presented. A cost-effectiveness plane and cost-effectiveness acceptability curve or –for dominant interventions the net monetary benefit function, should be presented. A Tornado diagram should show the most important contributors to the variability of the estimated incremental cost-effectiveness/cost-utility ratio. Uncertainty in economic evaluations of healthcare interventions is omnipresent, and should be properly described and accounted for in the submitted pharmacoeconomic file. Uncertainty is usually divided into three broad areas: (1) methodological uncertainty coming from the analytical methods chosen to perform the evaluation (e.g. discount rate or extrapolation methods; this is usually handled by presenting results from a methodological reference case, as described in Table 1, and other scenarios) (2) the data uncertainty coming from variability in sample data (handled via statistical analyses) or from uncertainty ranges chosen for non sample data (handled via sensitivity analyses) (3) uncertain generalizability of the study results to other populations and/or other contexts (handled via descriptive external validity assessment). Each of these three areas of uncertainty should be specifically addressed in the economic evaluation. Methodological uncertainty arising from the applied discount rate or the extrapolation method used in models should be tested using scenario analysis. This is comparable to one-way sensitivity analysis, where only one parameter is changed (the discount rate or the assumed effectiveness in the extended time period). KCE reports 78 Pharmacoeconomic guidelines 27 Data uncertainty applies to trial-based pharmacoeconomic evaluations as well as to models. In trial-based pharmacoeconomic evaluations, statistical analyses can be used to estimate the uncertainty around individual cost and effects data due to sampling variability. Detailed descriptive statistics, showing the distribution and variability of costs and effects data, should be presented. In case of modelling, sensitivity analysis can help determining the importance of the different assumptions behind the model on the results. Probabilistic sensitivity analyses should be performed on all uncertain parameters in a model; i.e. one probabilistic sensitivity analysis where all uncertain parameters are allowed to vary according to a predefined distribution, e.g. by means of Monte Carlo simulations. Distributions used for the uncertain modelling parameters should be justified. For composite measures, such as total costs, the different components with their respective distributions should be included in the sensitivity analysis if applicable. The probabilistic sensitivity analysis should be performed on the reference case and the alternative scenarios such as the scenarios related to the assumed effectiveness of the therapy in extended time periods. The central estimate of the ICER results directly from the probabilistic sensitivity analysis as the mean of the simulated ICERs. This is not necessary equal or close to the ratio of the mean incremental cost and mean incremental effect, which is the deterministic version of the ICER. A deterministic ICER can be presented if the Monte Carlo simulations fall in different quadrants of the cost-effectiveness plane. In addition to probabilistic sensitivity analyses, a scenario or univariable sensitivity analysis could be performed on modelling parameters that are decisive for the costeffectiveness ratio such as the price of the product and the discount rate for costs and outcomes. For each scenario a probabilistic sensitivity analysis can be easily performed and hence results can be presented with their 95% credibility interval. Values and distributions of other parameters can be kept as in the reference case analysis for these scenarios. There is no need to present all possible combinations of all scenarios. Hence, if one scenario changes the discount rates and another scenario changes the price of the product, it is not necessary to present a scenario where both the price and the discount rates have been changed compared to the reference case. The applicant is free to present additional univariable sensitivity or scenario analyses if these are deemed relevant. Appropriate justification of the additional analyses should be provided. In the case of observed cost and effects data in a trial based pharmacoeconomic study state-of-the-art methods should be used for the estimation of the confidence interval around the incremental cost-effectiveness ratio (see technical note in appendix 4). ICERs of pharmaceutical products for which a pharmacoeconomic evaluation is requested are expected to belong to the North-East quadrant of the cost-effectiveness plane (more costly and more effective treatment). The cost-effectiveness plane, with the results of the Monte Carlo simulations or bootstrapping, should always be presented, both for the cost-per-QALY gained and for the cost-per-LYG. A cost-effectiveness acceptability curve should be presented in order to show the probability that the treatment is cost-effective, given varying theoretical threshold values for the cost-effectiveness ratio. If the central estimate of the ICER belongs to the south-east quadrant (more effective and less costly), then the natural ordering of ICERs (the lower the ICER, the better) does not hold any more. Therefore, in case of a negative ICER, it is suggested to present the incremental net monetary benefit (NMB) function and report the incremental costs and effects separately. If the results of the probabilistic sensitivity analysis shows a negative lower bound for the ICER, the incremental costs (or savings) and incremental effects (or harms) should be reported separately for the lower bound of the 95% credibility interval, the point estimate and the upper bound of the 95% credibility interval. In addition, the percentage of the simulations where a negative ICER was found should be reported (if the simulated results fall in different quadrants of the cost-effectiveness plane, a separate percentage for each quadrant should be reported). 28 Pharmacoeconomic guidelines KCE reports 78 The relative impact of uncertain variables on the ICER should be presented by means of a Tornado diagram. 4.13 GUIDELINE 12: DISCOUNT RATE Future costs should be discounted at a rate of 3%; future benefits at a rate of 1.5%. To