We aimed to assess the extent to which economic evaluations of chronic obstructive pulmonary disease treatments incorporated patient-informed value elements (PIVEs) and value flower elements (VFEs), and to examine their association with economic outcomes. A targeted search of PubMed, Embase, and the Tufts Cost-Effectiveness Analysis Registry identified full-length economic evaluations of pharmacologic and non-pharmacologic chronic obstructive pulmonary disease treatments published between 2018 and 2026. Studies were reviewed for inclusion of PIVEs and VFEs. Wilcoxon rank-sum tests and exploratory linear regression analyses examined associations between PIVEs, VFEs, and economic outcomes. Of 58 studies, 57 were cost-effectiveness or cost-utility analyses conducted from a healthcare system perspective (n = 49). Physical endurance was included in all studies, whereas other PIVEs, including side effects (n = 17), access to care (n = 17), explanation of benefits and risks (n = 13), new therapeutic options (n = 11), and willingness to pay (n = 5), were less commonly considered. Among VFEs, quality-adjusted life-years (n = 55) and net cost (n = 57) predominated, while adherence-improving factors (n = 18), productivity (n = 14), reduction in uncertainty (n = 3), and severity of disease (n = 1) were inconsistently incorporated. Only one study reported direct patient engagement. Exploratory analyses found no consistent association between inclusion of PIVEs or VFEs and economic outcomes. Economic evaluations of chronic obstructive pulmonary disease treatments predominantly relied on net costs and quality-adjusted life-years, with limited incorporation of broader PIVEs or VFEs and rare patient engagement. The influence of broader value elements on economic outcomes remains uncertain. Future research should evaluate challenges to incorporating context-relevant PIVEs and VFEs and their implications for economic evaluation and healthcare decision making.
Coverage with evidence development (CED) is used to manage uncertainty in new health technologies, but its implementation varies across settings. Some evidence-development arrangements restrict access to patients participating in randomized controlled trials (RCTs), whereas others provide broader conditional coverage while real-world evidence (RWE) is generated concurrently. RWE may be generated more quickly than trial evidence, often using larger samples and at lower cost. However, it is vulnerable to bias such as unmeasured confounding, and both the magnitude and direction of such bias are typically unknown. Decision-makers therefore lack an ex ante framework for determining when RWE-based CED is preferable to RCT-based CED. We develop a decision-analytic framework that extends value-of-information methods by incorporating an explicit bias parameter to capture structural distortion in RWE treatment effect estimates. Drawing on quantitative bias analysis, the framework defines policy indifference bias thresholds as the primary decision metric, identifying the maximum tolerable bias for RWE-based CED to remain preferred. The framework also quantifies, as a complementary measure, the probability of RCT-based CED dominance, defined as the ex ante probability that the RCT-based strategy is more favorable even when RWE is unbiased. A case study illustrates how these two metrics are determined.
Chimeric antigen receptor T cell (CAR T) therapies pose challenges for economic evaluation because of complex treatment pathways and uncertainty around long-term outcomes. Within health technology assessment (HTA) bodies such as the National Institute for Health and Care Excellence (NICE), these features have led to recurring methodological debates. To identify methodological challenges specific to the HTA of CAR T therapies within NICE technology appraisals and to develop Delphi-informed recommendations to support more consistent modelling practice. A three-phase mixed-methods study was conducted. Phase 1 involved qualitative content analysis of published NICE appraisals of CAR T therapies to identify methodological issues and solutions. Phase 2 used a modified Delphi to determine which issues were specific to CAR T therapies and to agree upon preferred methodological approaches. Phase 3 involved clinical sense-checking of the preferred methodological approaches. Phase 1 reviewed nine NICE appraisals, identifying 16 issues around survival modelling, cure assumptions, costing and long-term outcomes. Ten issues were specified in relation to CAR T. Phase 2 identified one further relevant issue. The preferred approaches included modelling from leukapheresis (85
Systematic reviews of health economic evaluations (SR-HEEs) are perceived as mandatory components of the health technology assessment (HTA) process in many jurisdictions. However, HTA agencies vary in how they prescribe reviews of HEEs should be conducted and the explicit purpose they serve. This article identifies the requirements of prominent HTA agencies, reports on the real-world conduct of SR-HEEs by health technology developers (HTDs), and explores their utility in the HTA process. International HTA guidelines were consulted, alongside a targeted review of SR-HEEs submitted to the National Institute for Health and Care Excellence (NICE) in 2024. While a systematic approach is a constant requirement to reviews of HEEs, it is defined inconsistently across agencies, with clinical and safety systematic reviews (SRs) receiving more rigid prescription. Consequently, SR-HEEs submitted to NICE demonstrated mixed methods. Despite these pragmatic deviations of SR-HEEs, External Assessment Groups (EAGs) raised no major concerns, and reimbursement decisions do not appear to be influenced. Our findings suggest a rigor gap between SR theory and practice, driven by the inherent heterogeneity of economic models, which renders quantitative pooling inappropriate. We submit that a more pragmatic approach to reviews of HEEs would be appropriate for HTA. To maximize utility and reduce resource waste, we propose a two-stage framework: early pragmatic “landscape mapping” to inform early model conceptualization, followed by a formal SR-HEE only when a specific useful research question is identified. This strategic realignment, possibly supported by emerging technologies, would enable HTA resources to be reallocated toward higher-value activities.
