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Health economists have written extensively on the design and implementation of coverage with evidence development (CED) schemes and have proposed theoretical frameworks based on cost-effectiveness modeling and value of information analysis. CED may aid decision-makers when there is uncertainty about the (cost-)effectiveness of a new health technology at the time of reimbursement. Medical devices are potential candidates for CED schemes, as regulatory regimes do not usually require the same level of efficacy and safety data normally needed for pharmaceuticals. The purpose of this research is to assess whether the actual practice of CED for medical devices in Europe meets the theoretical principles proposed by health economists and whether theory and practice can be more closely aligned. Based on decision-makers' perceptions of the challenges associated with CED schemes, plus examples from the schemes themselves, we discuss a series of proposals for assessing the desirability of schemes, their design, implementation, and evaluation. These proposals, while reflecting the practical challenges with developing CED programs, embody many of the principles suggested by economists and should support decision-makers in dealing with uncertainty about the real-world performance of devices., (© 2022 The Authors. Health Economics published by John Wiley & Sons Ltd.)

Health economic modeling of novel technology at the early stages of a product lifecycle has been used to identify technologies that are likely to be cost-effective. Such early assessments are challenging due to the potentially limited amount of data. Modelers typically conduct uncertainty analyses to evaluate their effect on decision-relevant outcomes. Current approaches, however, are limited in their scope of application and imposes an unverifiable assumption, that is, uncertainty can be precisely represented by a probability distribution. In the absence of reliable data, an approach that uses the fewest number of assumptions is desirable. This study introduces a generalized approach for quantifying parameter uncertainty, that is, probability bound analysis (PBA), that does not require a precise specification of a probability distribution in the context of early-stage health economic modeling. We introduce the concept of a probability box (p-box) as a measure of uncertainty without necessitating a precise probability distribution. We provide formulas for a p-box given data on summary statistics of a parameter. We describe an approach to propagate p-boxes into a model and provide step-by-step guidance on how to implement PBA. We conduct a case and examine the differences between the status-quo and PBA approaches and their potential implications on decision-making., (© 2022 John Wiley & Sons Ltd.)

Decisions to adopt medical devices at the hospital level have consequences for health technology assessment (HTA) on system level and are therefore important to decision makers. Our aim was to investigate the characteristics of organizations and individuals that are more inclined to adopt and utilize cardiovascular devices based on a comprehensive analysis of environmental, organizational, individual, and technological factors and to identify corresponding implications for HTA. Seven random intercept hurdle models were estimated using the data obtained from 1249 surveys completed by members of the European Society of Cardiology. The major findings were that better manufacturer support increased the adoption probability of 'new' devices (i.e. in terms of CE mark approval dates), and that budget pressure increased the adoption probability of 'old' devices. Based on our findings, we suggest investigating the role of manufacturer support in more detail to identify diffusion patterns relevant to HTA on system level, to verify whether it functions as a substitute for medical evidence of new devices, and to receive new insights about its relationship with clinical effectiveness and cost-effectiveness. © 2017 The Authors. Health Economics published by John Wiley & Sons, Ltd., (© 2017 The Authors. Health Economics published by John Wiley & Sons, Ltd.)

Changes in performance due to learning may dynamically influence the results of a technology evaluation through the change in effectiveness and costs. In this study, we estimate the effect of learning using the example of two minimally invasive treatments of abdominal aortic aneurysms: endovascular aneurysm repair (EVAR) and fenestrated EVAR (fEVAR). The analysis is based on the administrative data of over 40,000 patients admitted with unruptured abdominal aortic aneurysm to more than 500 different hospitals over the years 2006 to 2013. We examine two patient outcomes, namely, in-hospital mortality and length of stay using hierarchical regression models with random effects at the hospital level. The estimated models control for patient and hospital characteristics and take learning interdependency between EVAR and fEVAR into account. In case of EVAR, we observe a significant decrease both in the in-hospital mortality and length of stay with experience accumulated at the hospital level; however, the learning curve for fEVAR in both outcomes is effectively flat. To foster the consideration of learning in health technology assessments of medical devices, a general framework for estimating learning effects is derived from the analysis. © 2017 The Authors. Health Economics published by John Wiley & Sons, Ltd., (© 2017 The Authors. Health Economics published by John Wiley & Sons, Ltd.)