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.

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.

For cost-utility analyses for children, health-related quality of life (HRQOL) is commonly measured using paediatric instruments such as EQ-5D-Y, yet associated value sets are typically derived from adult preferences. This practice raises a fundamental normative question: whose preferences, and thus whose values, should count when valuing child HRQOL? Recent academic critique has focused on whether children have the fundamental right and ability to provide preferences and hence should be involved in valuing child HRQOL, yet the default position, that preferences of adult members of the general public should (at least partially) determine the importance of changes in child HRQOL, remains unchallenged. This perspective critically examines the role adults have in the valuation of child HRQOL. We first revisit the dominant justification for adult involvement in valuation, i.e. the taxpayer argument, and show that recent empirical evidence from public, stakeholder and expert studies offers little support for this rationale. We then explore alternative normative arguments for involving adults, and propose a typology of ten distinct roles adults could take in valuation of child HRQOL, from being the sole source of preference information to acting as partners, caretakers or only gatekeepers that enable children themselves to value child HRQOL. We argue that greater conceptual clarity about why and how adults are involved is essential for the future development of paediatric value sets for use in economic evaluations, thereby strengthening the legitimacy, transparency and practical use of paediatric HRQOL instruments in policy and decision making, and identify several high priority research gaps.

Systematic reviews of economic evaluations are increasingly used to inform healthcare decision making, yet approaches for a cost-effectiveness meta-analysis vary substantially, and clear methodological guidance remains limited. Differences in country income level, currency, cost reference year, analytic perspective, time horizon, and outcome measures, as well as incomplete reporting of uncertainty, complicate the pooling and interpretation of pooled cost-effectiveness estimates. This tutorial provides a structured reproducible workflow for conducting a cost-effectiveness meta-analysis of trial-based economic evaluations using the incremental net benefit framework. Step-by-step guidance is provided for harmonising costs across countries and time (using purchasing power parity and inflation adjustments, respectively), estimating or imputing missing variances of incremental costs and effects, and pooling incremental net benefits across a range of willingness-to-pay thresholds. Fully annotated R scripts and practical examples are provided to demonstrate the meta-analysis of incremental costs, effects, and net benefits, and the generation of pooled cost-effectiveness acceptability curves to depict decision uncertainty. Stratified and sensitivity analyses are described to explore contextual heterogeneity and assess robustness, respectively. By providing transparent methods and reproducible tools, this tutorial supports a more consistent and interpretable cost-effectiveness meta-analysis, while highlighting important methodological challenges.