Background: Reporting standards of discrete choice experiments (DCEs) in health have not kept pace with the growth of this method, with multiple reviews calling for better reporting to improve transparency, assessment of validity and translation. A key missing piece has been the absence of a reporting checklist that details minimum standards of what should be reported, as exists for many other methods used in health economics.
Methods: This paper reports the development of a reporting checklist for DCEs in health, which involved a scoping review to identify potential items and a Delphi consensus study among 45 DCE experts internationally to select items and guide the wording and structure of the checklist. The Delphi study included a best-worst scaling study for prioritisation.
Conclusions: The final checklist is presented along with guidance on how to apply it. This checklist can be used by authors to ensure that sufficient detail of a DCE's methods are reported, providing reviewers and readers with the information they need to assess the quality of the study for themselves. Embedding this reporting checklist into standard practice for health DCEs offers an opportunity to improve consistency of reporting standards, thereby enabling transparency of review and facilitating comparison of studies and their translation into policy and practice.
There is increasing interest in the use of cure modelling to inform health technology assessment (HTA) due to the development of new treatments that appear to offer the potential for cure in some patients. However, cure models are often not included in evidence dossiers submitted to HTA agencies, and they are relatively rarely relied upon to inform decision-making. This is likely due to a lack of understanding of how cure models work, what they assume, and how reliable they are. In this tutorial we explain why and when cure models may be useful for HTA, describe the key characteristics of mixture and non-mixture cure models, and demonstrate their use in a range of scenarios, providing Stata code. We highlight key issues that must be taken into account by analysts when fitting these models and by reviewers and decision-makers when interpreting their predictions. In particular, we note that flexible parametric non-mixture cure models have not been used in HTA, but they offer advantages that make them well suited to an HTA context when a cure assumption is valid but follow-up is limited.
Background: Health systems are moving towards value-based care, implementing new care models that allegedly aim beyond patient outcomes. Therefore, a policy and academic debate is underway regarding the definition of value in healthcare, the inclusion of costs in value metrics, and the importance of each value element. This study aimed to define healthcare value elements and assess their relative importance (RI) to the public in England.
Method: Using data from 26 semi-structured interviews and a literature review, and applying decision-theory axioms, we selected a comprehensive and applicable set of value-based elements. Their RI was determined using two discrete choice experiments (DCEs) based on Bayesian D-efficient DCE designs, with one DCE incorporating healthcare costs expressed as income tax rise. Respondent preferences were analysed using mixed logit models.
Results: Six value elements were identified: additional life-years, health-related quality of life, patient experience, target population size, equity, and cost. The DCE surveys were completed by 402 participants. All utility coefficients had the expected signs and were statistically significant (p < 0.05). Additional life-years (25.3%; 95% confidence interval [CI] 22.5-28.6%) and patient experience (25.2%; 95% CI 21.6-28.9%) received the highest RI, followed by target population size (22.4%; 95% CI 19.1-25.6%) and quality of life (17.6%; 95% CI 15.0-20.3%). Equity had the lowest RI (9.6%; 95% CI 6.4-12.1%), decreasing by 8.8 percentage points with cost inclusion. A similar reduction was observed in the RI of quality of life when cost was included.
Conclusion: The public prioritizes value elements not captured by conventional metrics, such as quality-adjusted life-years. Although cost inclusion did not alter the preference ranking, its inclusion in the value metric warrants careful consideration.
Background: Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST).
Methods: A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.
Results: The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).
Conclusions: Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.
Introduction: Immunocompromised host pneumonia (ICHP) is an important cause of morbidity and mortality, yet usual care (UC) diagnostic tests often fail to identify an infectious etiology. A US-based, multicenter study (PICKUP) among ICHP patients with hematological malignancies, including hematological cell transplant recipients, showed that plasma microbial cell-free DNA (mcfDNA) sequencing provided significant additive diagnostic value.
Aim: The objective of this study was to perform a cost-effectiveness analysis (CEA) of adding mcfDNA sequencing to UC diagnostic testing for hospitalized ICHP patients.
Methods: A semi-Markov model was utilized from the US third-party payer's perspective such that only direct costs were included, using a lifetime time horizon with discount rates of 3% for costs and benefits. Three comparators were considered: (1) All UC, which included non-invasive (NI) and invasive testing and early bronchoscopy; (2) All UC & mcfDNA; and (3) NI UC & mcfDNA & conditional UC Bronch (later bronchoscopy if the initial tests are negative). The model considered whether a probable causative infectious etiology was identified and if the patient received appropriate antimicrobial treatment through expert adjudication, and if the patient died in-hospital. The primary endpoints were total costs, life-years (LYs), equal value life-years (evLYs), quality-adjusted life-years (QALYs), and the incremental cost-effectiveness ratio per QALY. Extensive scenario and probabilistic sensitivity analyses (PSA) were conducted.
Results: At a price of $2000 (2023 USD) for the plasma mcfDNA, All UC & mcfDNA was more costly ($165,247 vs $153,642) but more effective (13.39 vs 12.47 LYs gained; 10.20 vs 9.42 evLYs gained; 10.11 vs 9.42 QALYs gained) compared to All UC alone, giving a cost/QALY of $16,761. NI UC & mcfDNA & conditional UC Bronch was also more costly ($162,655 vs $153,642) and more effective (13.19 vs 12.47 LYs gained; 9.96 vs 9.42 evLYs gained; 9.96 vs 9.42 QALYs gained) compared to All UC alone, with a cost/QALY of $16,729. The PSA showed that above a willingness-to-pay threshold of $50,000/QALY, All UC & mcfDNA was the preferred scenario on cost-effectiveness grounds (as it provides the most QALYs gained). Further scenario analyses found that All UC & mcfDNA always improved patient outcomes but was not cost saving, even when the price of mcfDNA was set to $0.
Conclusions: Based on the evidence available at the time of this analysis, this CEA suggests that mcfDNA may be cost-effective when added to All UC, as well as in a scenario using conditional bronchoscopy when NI testing fails to identify a probable infectious etiology for ICHP. Adding mcfDNA testing to UC diagnostic testing should allow more patients to receive appropriate therapy earlier and improve patient outcomes.
We propose a short-cut heuristic approach to rapidly estimate value of information (VOI) using information commonly reported in a research funding application to make a case for the need for further evaluative research. We develop a "Rapid VOI" approach, which focuses on uncertainty in the primary outcome of clinical effectiveness and uses this to explore the health consequences of decision uncertainty. We develop a freely accessible online tool, Rapid Assessment of the Need for Evidence (RANE), to allow for the efficient computation of the value of research. As a case study, the method was applied to a proposal for research on shoulder pain rehabilitation. The analysis was included as part of a successful application for research funding to the UK National Institute for Health and Care Research. Our approach enables research funders and applicants to rapidly estimate the value of proposed research. Rapid VOI relies on information that is readily available and reported in research funding applications. Rapid VOI supports research prioritisation and commissioning decisions where there is insufficient time and resources available to develop and validate complex decision-analytic models. The method provides a practical means for implementing VOI in practice, thus providing a starting point for deliberation and contributing to the transparency and accountability of research prioritisation decisions.
Background and objective: Multiple myeloma is a rare incurable hematological cancer in which most patients relapse or become refractory to treatment. This systematic literature review aimed to critically review the existing economic models used in economic evaluations of systemic treatments for relapsed/refractory multiple myeloma and to summarize how the models addressed differences in the line of therapy and exposure to prior treatment.
Methods: Following a pre-approved protocol, literature searches were conducted on 17 February, 2023, in relevant databases for models published since 2014. Additionally, key health technology assessment agency websites were manually searched for models published as part of submission dossiers since 2018. Reported information related to model conceptualization, structure, uncertainty, validation, and transparency were extracted into a pre-defined extraction sheet.
Results: In total, 49 models assessing a wide range of interventions across multiple lines of therapy were included. Only five models specific to heavily pre-treated patients and/or those who were refractory to multiple treatment classes were identified. Most models followed a conventional simple methodology, such as partitioned survival (n = 28) or Markov models (n = 9). All included models evaluated specific interventions rather than the whole treatment sequence. Where subsequent therapies were included in the model, these were generally only considered from a cost and resource use perspective. The models generally used overall and progression-free survival as model inputs, although data were often immature. Sensitivity analyses were frequently reported (n = 41) whereas validation was only considered in less than half (n = 19) of the models.
Conclusions: Published economic models in relapsed/refractory multiple myeloma rarely followed an individual patient approach, mainly owing to the higher need for complex data assumptions compared with simpler modeling approaches. As many patients experience disease progression on multiple treatment lines, there is a growing need for modeling complex treatment strategies, leading to more sophisticated approaches in the future. Maintaining transparency, high reporting standards, and thorough analyses of uncertainty are crucial to support these advancements.
Background: Amyotrophic lateral sclerosis (ALS) is a devastating disease which leads to loss of muscle function and paralysis. Historically, clinical drug development has been unsuccessful, but promising disease-modifying therapies (DMTs) may be on the horizon.
Objectives: The aims of this study were to estimate survival, quality-adjusted life-years (QALYs) and costs under current care, and to explore the conditions under which new therapies might be considered cost effective.
Methods: We developed a health economic model to evaluate the cost effectiveness of future ALS treatments from a UK National Health Service and Personal Social Services perspective over a lifetime horizon using data from the ALS-CarE study. Costs were valued at 2021/22 prices. Two hypothetical interventions were evaluated: a DMT which delays progression and mortality, and a symptomatic therapy which improves utility only. Sensitivity analysis was conducted to identify key drivers of cost effectiveness.
Results: Starting from King's stage 2, patients receiving current care accrue an estimated 2.27 life-years, 0.75 QALYs and lifetime costs of £68,047. Assuming a 50% reduction in progression rates and a UK-converted estimate of the price of edaravone, the incremental cost-effectiveness ratio for a new DMT versus current care is likely to exceed £735,000 per QALY gained. Symptomatic therapies may be more likely to achieve acceptable levels of cost effectiveness.
Conclusions: Regardless of efficacy, DMTs may struggle to demonstrate cost effectiveness, even at a low price. The cost effectiveness of DMTs is likely to be strongly influenced by drug price, the magnitude and durability of relative treatment effects, treatment starting/stopping rules and any additional utility benefits over current care.
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