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Evidence about cost-effectiveness is increasingly being used to inform decisions about the funding of new technologies that are usually implemented as guidelines from centralized decision-making bodies. However, there is also an increasing recognition for the role of patients in determining their preferred treatment option. This paper presents a method to estimate the value of implementing a choice-based decision process using the cost-effectiveness analysis toolbox. This value is estimated for 3 alternative scenarios. First, it compares centralized decisions, based on population average cost-effectiveness, against a decision process based on patient choice. Second, it compares centralized decision based on patients' subgroups versus an individual choice-based decision process. Third, it compares a centralized process based on average cost-effectiveness against a choice-based process where patients choose according to a different measure of outcome to that used by the centralized decision maker. The methods are applied to a case study for the management of acute coronary syndrome. It is concluded that implementing a choice-based process of treatment allocation may be an option in collectively funded health systems. However, its value will depend on the specific health problem and the social values considered relevant to the health system., (Copyright © 2017 John Wiley & Sons, Ltd.)

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., (Copyright © 2017 John Wiley & Sons, Ltd.)

Organisations across diverse health care systems making decisions about the funding of new medical technologies face extensive stakeholder and political pressures. As a consequence, there is quite understandable pressure to take account of other attributes of benefit and to fund technologies, even when the opportunity costs are likely exceed the benefits they offer. Recent evidence suggests that NICE technology appraisal is already approving drugs where more health is likely to be lost than gained. Also, NICE recently proposed increasing the upper bound of the cost-effectiveness threshold to reflect other attributes of benefit but without a proper assessment of the type of benefits that are expected to be displaced. It appears that NICE has taken a direction of travel, which means that more harm than good is being, and will continue to be, done, but it is unidentified NHS patients who bear the real opportunity costs., (Copyright © 2014 John Wiley & Sons, Ltd.)

Brouwer and colleagues [1] argue that the reasons for specifying an equal discount rate for health outcomes and costs in the recent guidance on methods of technology appraisal issued by the National Institute for Clinical Excellence (NICE) [2] is both opaque and wrong. They argue that a lower rate should apply to health outcomes like QALYs. It is also claimed that the guidance on discounting represents a step backwards, that is both inconsistent with current theoretical insights and will prejudice the outcome of cost-effectiveness studies of preventive interventions.The reasoning behind the use of equal discount rates for costs and health outcomes is indeed not well developed in the published guidance. Nor does it reflect the debate that underpinned the guidance. We therefore welcome the opportunity to explain more completely the rationale in the minds of the principal authors of the current guidance., (Copyright (c) 2006 John Wiley & Sons, Ltd.)

Cost-effectiveness analysis (CEA) in health care is increasingly conducted alongside multicentre and multinational randomised controlled clinical trials (RCTs). The increased use of stochastic CEA is designed to account for between-patient sampling variability in cost-effectiveness data assuming that observations are independently distributed. However, between-location variability in cost-effectiveness may result if there is a hierarchical structure in the data; that is, if there is correlation in costs and outcomes between patients recruited in particular locations. This may be expected in multi-location trials given that centres and countries often differ in factors such as clinical practice, patient case-mix and the unit costs of delivering health care. A failure to acknowledge this feature may lead to misleading conclusions in a trial-based economic study. Multilevel modelling (MLM) is an analytical framework that can be used to handle hierarchical cost-effectiveness data. Using data from a recently conducted economic analysis, this paper shows how multilevel modelling can be used to obtain (a) more appropriate estimates of the population average incremental cost-effectiveness and associated standard errors compared to standard stochastic CEA; and (b) location-specific estimates of incremental cost-effectiveness which can be used to explore appropriately the variability between centres/countries of the cost-effectiveness results., (Copyright 2004 John Wiley & Sons, Ltd)

In trial-based cost-effectiveness analysis baseline mean utility values are invariably imbalanced between treatment arms. A patient's baseline utility is likely to be highly correlated with their quality-adjusted life-years (QALYs) over the follow-up period, not least because it typically contributes to the QALY calculation. Therefore, imbalance in baseline utility needs to be accounted for in the estimation of mean differential QALYs, and failure to control for this imbalance can result in a misleading incremental cost-effectiveness ratio. This paper discusses the approaches that have been used in the cost-effectiveness literature to estimate absolute and differential mean QALYs alongside randomised trials, and illustrates the implications of baseline mean utility imbalance for QALY calculation. Using data from a recently conducted trial-based cost-effectiveness study and a micro-simulation exercise, the relative performance of alternative estimators is compared, showing that widely used methods to calculate differential QALYs provide incorrect results in the presence of baseline mean utility imbalance regardless of whether these differences are formally statistically significant. It is demonstrated that multiple regression methods can be usefully applied to generate appropriate estimates of differential mean QALYs and an associated measure of sampling variability, while controlling for differences in baseline mean utility between treatment arms in the trial., (Copyright 2004 John Wiley & Sons, Ltd)