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OBJECTIVES: Health technology assessment (HTA) in the UK often requires that health-related quality of life is considered in a cost-utility analysis (CUA). Most studies contain missing data at some level. Missing utility data can misrepresent the denominator of the ICER. Despite this, there are no definitive guidelines on how to manage missingness for UK HTA, and analysts rely on judgment to address missingness. We intend for this research to formalise our recommendations for dealing with missing utility data for UK HTA.
METHODS: A simulated individual patient dataset, similar to those used to estimate utility values for a CUA submission for HTA in the UK, was developed. With this dataset, we simulated missingness at various levels and by different mechanisms. We assessed the performance of: complete case analysis (CCA), mean square estimation (MSE), linear mixed modelling (LMM) and multiple imputation via chained equations (MICE), and used this to make recommendations for handling and reporting of missing data in IPD that will be submitted to decision-makers in the UK.
RESULTS: Regardless of the mechanism or the level of missingness, MICE and LMM always resulted in substantially less error when calculating health state utility. Where data were missing at 30%, CCA and MSE were often associated with a substantially higher mean difference than MICE and LMM. The standard deviation was always substantially depressed regardless of mechanism or level of missingness when LMM was used.
CONCLUSIONS: It is our recommendation that an assessment of the mechanism and magnitude of missingness be made and reported for all data sets that are used to inform economic models for HTA in the UK as a minimum standard. Further, we recommend that CCA is almost never appropriate and our research, echoed by others supports the use of MICE as standard.
OBJECTIVES: Randomized controlled trials (RCTs) are the gold standard for evaluating effectiveness of interventions. Interventional single-arm trials (SATs) are increasingly being considered, despite a lack of agreement on their validity and position in the hierarchy of evidence. Notably, it is unclear whether SATs are superior to observational single-arm studies (case series). We investigated whether there are systematic differences in outcome and between-study heterogeneity for SATs compared with case series.
METHODS: We conducted a pragmatic literature review for systematic reviews (SRs) of pharmacological interventions including single-arm studies. A single reviewer identified SRs and extracted primary study characteristics and outcome data. For each SR, meta-analysis of dichotomous outcomes was conducted, with sub-group analysis of the included SATs versus case series. To investigate whether statistical heterogeneity was explained by clinical heterogeneity or indicated bias, clinically different primary studies were removed in a sensitivity analysis.
RESULTS: 13 SRs were included. When primary studies were sub-grouped by study design, there was no significant difference for SATs versus case series across SRs (risk difference -0.02, 95% CI 0.09, 0.05). There were high levels of between-study heterogeneity within both SATs (median I2: 55%) and case series (median I2: 77%). When clinically heterogenous studies were removed, effect size tended to be greater for case series, but not significantly so (risk difference -0.071, 95% CI -0.161, 0.019). Levels of within-group statistical heterogeneity remained high, suggesting that bias may have been a moderator of effect in both SATs and case series.
CONCLUSIONS: There do not appear to be systematic differences in outcome between SATs and case series. However, levels of heterogeneity in effect size are high within both designs, even after attempts to reduce clinical heterogeneity, indicating that bias may have an impact on outcomes. Future work should utilize larger samples and additional methods to further clarify the relative validity of single-arm designs.
OBJECTIVES: The health technology assessment (HTA) submission process is becoming increasingly diverse between countries. This study assesses the HTA requirements in Australia, Canada, England and Spain: four countries where pharmacoeconomic evidence forms an integral part of the value assessment. Technology developers can use these insights to identify where efficiencies can be made in the global market access strategy for new technologies, such as when to submit HTA dossiers.
METHODS: A pragmatic review and desk-based research were conducted in May 2024. Published articles, HTA guidelines, process documents, conference abstracts, and white papers were reviewed to identify country-specific processes. Where available, data were extracted about the general submission process and stakeholders involved (including regulatory, HTA and pricing authorities), as well as the clinical and pharmacoeconomic evidence requirements for HTA submission. Comparisons of the median time from marketing authorization to HTA decision within each country were also conducted. The key findings and between-country differences were synthesized in a narrative summary.
RESULTS: The review identified several areas with implications for market access strategy. All countries offer a parallel regulatory/HTA process. The median HTA review time between 2014 and 2018 was shortest in Australia (125 days) and longest in England (266 days). Australia demonstrated general consistency in HTA review time between submissions (interquartile range = 9 days), and England had the most variation in the duration of HTA reviews (interquartile range = 216 days). All countries require comparative clinical evidence within the indication and pharmacoeconomic evidence. A cost-utility analysis is the preferred analytical tool. However, England also readily accepts cost-effectiveness analysis.
CONCLUSIONS: While the median HTA review time varied between countries, similar requirements in clinical and pharmacoeconomic evidence allow efficiencies in the preparation of submission documentation. Future research should investigate the impact of the EU HTA Regulation on market access and how this could affect strategic decision making.
OBJECTIVES: Climate breakdown is affecting human health globally. The National Health Service (NHS) generates 26 million tonnes of CO2e per annum equivalent to the size of Croatia's annual emissions. Healthcare suppliers possess a sizeable opportunity to support health technology environmental sustainability (HTES) underpinned by leaner pathways and resource optimization. HTA is developing approaches to include environmental sustainability (ES). Reporting a broad range of environmental outcomes is important, but there is a lack of published guidance. This study aims to develop recommendations for decision makers on how broader environmental criteria should be included in HTA, based on the technical validation of an environmental model of Aurora EV-ICD by Medtronic.
METHODS: The Medtronic environmental model estimates the impact of the device on CO2e, water usage and waste volumes over a 10-year time horizon. A multi-step process was used to internally validate the model, using a specifically designed checklist for HTES models being reported as an information conduit. Recommendations were provided to support appropriate reporting of the data to the NHS.
RESULTS: The model structure was deemed appropriate and suitable for submission to the NHS. Recommendations for reporting ES to the NHS include: 1) using and referencing multiple environmental management guidelines for reporting CO2e to ensure transparency and reproducibility; 2) applying the same principles to quantify other environmental outcomes that will enable a more holistic evaluation of unintended consequences, including human health impact, resource use and biodiversity loss; 3) using life cycle assessment software (such as OpenLCA) to overcome data challenges and to facilitate reporting appropriate and comprehensive environmental endpoint categories.
CONCLUSIONS: This multi-perspective collaboration between industry and researchers supports ES in HTA framework development. These recommendations can be used by decision makers to aid the inclusion of environmental models that supports reporting a broader range of environmental outcomes over a long-term time horizon.
BACKGROUND: The EARLY-AF (NCT02825979), STOP AF First (NCT03118518), and Cryo-FIRST (NCT01803438) randomised controlled trials (RCTs) demonstrated that cryoballoon pulmonary vein isolation reduces atrial fibrillation (AF) recurrence compared with antiarrhythmic drugs (AADs) in patients with symptomatic paroxysmal atrial fibrillation (PAF). The present study developed a cost-effectiveness model (CEM) of first-line cryoablation compared with first-line AADs for PAF, from the Canadian health care payer's perspective.
METHODS: Data from the 3 RCTs were analysed to estimate key CEM parameters. The model structure used a decision tree for the first 12 months and a Markov model with a 3-month cycle length for the remaining lifetime time horizon. Costs were set at 2023 Canadian dollars, health benefits were expressed as quality-adjusted life years (QALYs), and both were discounted 3% annually. Probabilistic sensitivity analysis (PSA) considered parameter uncertainty.
RESULTS: The statistical analysis estimated that first-line cryoablation generates a 47% reduction (P < 0.001) in the rate of AF recurrence, a 73% reduction in the rate of subsequent ablation (P < 0.001), and a 4.3% (P = 0.025) increase in health-related quality of life, compared with first-line AADs. The PSA indicates that an individual treated with first-line cryoablation accrues less costs (-$3,862) and more QALYs (0.19) compared with first-line AADs. Cryoablation is cost-saving in 98.4% of PSA iterations and has a 99.9% probability of being cost-effective at a cost-effectiveness threshold of $50,000 per QALY gained. Cost-effectiveness results were robust to changes in key model parameters. CONCLUSIONS: First-line cryoballoon ablation is cost-effective when compared with AADs for patients with symptomatic PAF.
OBJECTIVES: Diabetes is a chronic condition that incurs substantial costs on health systems. The increasing prevalence of diabetes means that costs will likely increase exponentially over the next few decades, with devastating effects on healthcare funding. This study reports on the increased costs, as well as the potential savings should there be a slowing down in the number of new cases.
METHODS: A recent study has reported that the annual cost of diabetes (including Type 1, Type 2 and gestational diabetes) in the United Kingdom is £10.7 billion, with an additional £3.3 billion indirect costs. We use current prevalence forecasts to estimate the future costs under a series of different assumptions for the change in prevalence rates. The estimates assume that there will be no changes to care management.
RESULTS: Using current forecast estimates, the total annual cost of diabetes is estimated to increase to £17.9 billion within fifteen years (an increase of 68%). If the rate of growth in prevalence could be reduced by just 1% (i.e. from 0.200% to 0.198%), then this would result in annual savings of £67.9 million by year 15. A 10% reduction in the growth of prevalence would results in savings of £678.7 million, whilst a 50% reduction in the growth of prevalence would save £3.4 billion annually.
CONCLUSIONS: Even very small changes in the annual increase in prevalence of diabetes can lead to substantial cost savings that can be reinvested in the health system. Larger reductions will have exponentially increased impact, potentially saving billions of pounds for the health system. This suggests that public health efforts to prevent diabetes should be prioritized in order to avoid devastating impacts on health system funding.
OBJECTIVES: It is common for the uncertainty in an economic evaluation to be 'tested' using one-way sensitivity analysis. A common method for reporting such analysis is to present a 'tornado diagram'. This research explores how useful this method is, and whether alternative approaches should be recommended.
METHODS: We examined the results sections of recent technology appraisals from the National Institute for Health and Care Excellence (NICE) in the United Kingdom. We recorded whether one-way sensitivity analysis was undertaken and, if so, how it was reported.
RESULTS: One-way sensitivity analysis was undertaken in all appraisals. In the vast majority of cases, the results were reported using tornado diagrams. In most cases, the ranges used were based on observed uncertainty for the parameter inputs (e.g. standard errors or confidence intervals) and in a small proportion, they were based on arbitrary ranges. However, in most cases, the diagram or table only showed the key output (e.g. incremental cost-effectiveness ratio or net health benefit) for the extreme values in the range, and no information was provided for intermediate values.
CONCLUSIONS: One-way sensitivity analysis is a tool to help decision makers understand the degree of uncertainty associated with model inputs, and how that uncertainty affects the model's outcomes. Showing the results only for the extreme values deprives the decision maker of useful information. For example, where a 95% confidence interval is used, the decision maker is only able to see the impact of the changes for outcomes that have a 2.5% likelihood of occurring (i.e. at each end of the range). Instead, tornado diagrams could take a number of alternative approaches to show the impact of different values along the plausible ranges. This could include, for example, decile values, inter-quartile ranges and threshold points. This would allow decision makers to have more constructive discussions around the impact of uncertainty.
