In long-term users, discontinuation of low-dose aspirin in the absence of major surgery or bleeding was associated with a >30% increased risk of cardiovascular events. Adherence to low-dose aspirin treatment in the absence of major surgery or bleeding is likely an important treatment goal.

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Purpose. Clinical practice variations and low implementation of effective and cost-effective health care technologies are a key challenge for health care systems and may lead to suboptimal treatment and health loss for patients. The purpose of this work was to subcategorize the expected value of perfect implementation (EVPIM) to enable estimation of the absolute and relative value of eliminating slow, low, and delayed implementation. Methods. Building on the EVPIM framework, this work defines EVPIM subcategories to estimate the expected value of eliminating slow, low, or delayed implementation. The work also shows how information on regional implementation patterns can be used to estimate the value of eliminating regional implementation variation. The application of this subcategorization is illustrated by a case study of the implementation of an antiplatelet therapy for the secondary prevention after myocardial infarction in Sweden. Incremental net benefit (INB) estimates are based on published cost-effectiveness assessments and a threshold of SEK 250,000 (£22,300) per quality-adjusted life year (QALY). Results. In the case study, slow, low, and delayed implementation was estimated to represent 22%, 34%, and 44% of the total population EVPIM (2941 QALYs or SEK 735 million), respectively. The value of eliminating implementation variation across health care regions was estimated to 39% of total EVPIM (1138 QALYs). Conclusion. Subcategorizing EVPIM estimates the absolute and relative value of eliminating different parts of suboptimal implementation. By doing so, this approach could help decision makers to identify which parts of suboptimal implementation are contributing most to total EVPIM and provide the basis for assessing the cost and benefit of implementation activities that may address these in future implementation of health care interventions.

This paper presents a conceptual framework to analyse the design of the cost-effectiveness appraisal process of new healthcare technologies. The framework characterises the appraisal processes as a diagnostic test aimed at identifying cost-effective (true positive) and non-cost-effective (true negative) technologies. Using the framework, factors that influence the value of operating an appraisal process, in terms of net gain to population health, are identified. The framework is used to gain insight into current policy questions including (a) how rigorous the process should be, (b) who should have the burden of proof, and (c) how optimal design changes when allowing for appeals, price reductions, resubmissions, and re-evaluations. The paper demonstrates that there is no one optimal appraisal process and the process should be adapted over time and to the specific technology under assessment. Optimal design depends on country-specific features of (future) technologies, for example, effect, price, and size of the patient population, which might explain the difference in appraisal processes across countries. It is shown that burden of proof should be placed on the producers and that the impact of price reductions and patient access schemes on the producer's price setting should be considered when designing the appraisal process.

matrices with four, seven, ten and twenty health states. Results: In theory, there is no generally applicable correct transformation method. Based on our simulations, SP resulted in the smallest transformation induced discrepancies for generated annual transition matrices for two treatment strategies (relative difference between matrices SP:4.66*10 216 ; C:0.00835). E showed slightly smaller discrepancies than SP when one of the direct transitions between health states was excluded. For longterm outcomes, the largest discrepancy occurred for estimated costs applying method C (up to 10%). For higher dimensional models, E performs best. Conclusions: In our breast-cancer example, matrix transformations (E, SP) perform better than transforming all transition probabilities separately (C). Transition probabilities based on alternative conversion methods should, therefore, be applied in sensitivity analyses to see the impact on model outcomes (ICER and proposed decision). There is a need to add alternative transformation methods to decision-analytic software tools.

perspective. METHODS: The IQVIA Core Diabetes Model (CDM) was calibrated to reproduce the outcomes from the EMPA-REG OUTCOME trial. Baseline characteristics and observed effects on physiological parameters (HbA1c, BMI, blood pressure, lipids) were used as inputs. Network meta-analysis provided the relative risks for cardiovascular outcomes with empagliflozin versus sitagliptin and saxagliptin. HbA1c progression for all arms was projected based on the EMPA-REG OUTCOME trial. The effects of the CVOTs were applied up until treatment switch (reaching HbA1c of 8.5%) after which, the UKPDS82 risk equations predicted events based on physiological parameters. Basal-bolus rescue therapy was assumed after reaching an HbA1c of 8.5%. UK event costs and quality of life data were taken from literature. Drug costs were from the British National Formulary and Monthly Index of Medical Specialities. Discounting of 3.5% was applied. RESULTS: CDM projected 5.923, 5.462, and 5.324 qualityadjusted life-years (QALYs) and 51,629GBP, 48,306GBP and 48,885GBP total lifetime costs for empagliflozin, sitagliptin and saxagliptin, respectively. The incremental cost-effectiveness ratio of empagliflozin versus sitagliptin and saxagliptin was 7,209GBP/QALY and 4,582GBP/QALY, respectively. One-way and probabilistic sensitivity analyses showed robustness of the results. CONCLUSIONS: The results of this cost effectiveness analysis suggested that empagliflozin + SoC was costeffective compared to sitagliptin + SoC and saxagliptin + SoC at a willingness to pay threshold of 20,000GBP/QALY.

Abstract:To manage the challenge of limited healthcare resources and unlimited demand for healthcare, decision makers utilise a variety of demand side policies, such as health technology appraisals and international reference pricing to regulate price and utilisation. By controlling price and utilisation demand side policies determine the earnings potential, and hence the incentives to invest in research and development (R&D) of new technologies. However, the impact of demand side policies on R&D incentives is seldom formally assessed.Based on the key assumption that intellectual property rights, i.e. patents, and expected rent are key drivers of pharmaceutical R&D, this work outlines a framework illustrating the link between demand side policies and pharmaceutical R&D incentives. By analysing how policies impact expected rent and consumer surplus, the framework is used to understand how commonly used demand side policies (including timing and length of reimbursement process, international reference pricing, parallel trade, and sequential adoption into clinical practice) may influence R&D incentives.The analysis demonstrates that delayed reimbursement decisions as well as sequential adoption into clinical practise may in fact reduce both expected rent and consumer surplus. It is also demonstrated how international reference pricing is likely to increase consumer surplus at the expense of lower rent and thus lower R&D incentives.Although this work illustrates the importance of considering how demand side policies may impact long-term R&D incentives, it is important to note that the purpose has not been to prescribe which demand side policies should be utilised or how. Rather, the main contribution is to illustrate the need for a structured approach to the analysis of the complex, and at times highly politicised question of how demand side policies ultimately influence population health, both in the short and in the long term.JEL classification: I18, F13, L12, O30

pembrolizumab) were reviewed nearly twice as frequently (35.8%) as TKIs (16.7%). All immunotherapies, except durvalumab, were evaluated by all studied countries, with a majority of decisions published as favorable (58.1%). High cost of immunotherapies was commonly cited as the reason for unfavorable decisions, while increased evidence for clinical benefit in patient subpopulations was often requested for mixed and neutral decisions. Conclusions: More than half of all HTA decisions were categorized as favorable. Immunotherapies were among the most commonly evaluated NSCLC treatments. Further emphasis on developing and evaluating robust health economic and clinical data is necessary as the landscape shifts towards precision medicine.