Objectives: Data needed for economic evaluations in healthcare are often subject to privacy regulations and confidentiality, limiting accessibility. This poses challenges for conducting, reviewing, and validating health economic evaluations. The use of “synthetic data” may solve this problem. Methods: An economic evaluation compared “shamectomy” with “usual care” for the prevention of a fictitious disease called shame. A data set (Dorg) was created, consisting of 1000 patients in the base case. Next, synthetic data (Dsyn) were created from Dorg. Dorg and Dsyn were used, separately, to inform a model-based economic evaluation, and the similarity of the results was assessed for various scenarios: different sizes of Dorg, order of synthetization, method of synthetization, number of synthesized data sets, and missing data. Results: With standard settings, incremental cost-effectiveness ratio (ICER)-results for shamectomy were €25 848/quality-adjusted life-year in Dorg and on average €25 857 in 500 Dsyns, 95% CI (€16 776; €60 021). In the base case, 15% of the generated Dsyns resulted in an ICER leading to a positive reimbursement decision, as opposed to a negative decision when using Dorg. With smaller Dorg data sets (n = 50 and n = 500), ICER ranges increased to 95% CI (negative; €151 542) and 95% CI (negative; €669 717), respectively. Conclusions: Outcomes and conclusions of economic analyses based on synthetic data may deviate from those obtained by using the original data. For data sets < 1000 patients, which are common, deviations may be substantial and lead to suboptimal policy decisions. Based on our results, we propose a stepwise approach to using synthetic data for model-based health economic evaluations, using a large number of synthetic data sets (ie, >100) with the same size as the original data.

Systematic literature reviews (SLRs) are fundamental for aggregating published evidence, identifying knowledge gaps and informing health economic evaluations, especially in the field of novel diagnostics where randomised controlled trials are commonly absent [1]. The global publishing output within the field of novel diagnostics is increasing rapidly owing to increasing efforts towards precision medicine. Simultaneously, the global publishing output within the field of health economics is increasing rapidly owing to increasing efforts towards value-based healthcare and present budget constraints. The number of publications focussing on the health economic impact of diagnostics almost doubled from 86,244 in 2010 to 152,404 in 2025 (PubMed search using MeSH terms ‘Diagnostic Techniques and Procedures’ and ‘Health Care Economics and Organizations’). Consequently, the workload of performing SLRs is increasing as the number of articles that requires screening grows larger. The average time to complete an SLR is over 15 months, while the proportion of truly relevant articles for data extraction may be as low as 1% of the total search results [2]. The high workload may reduce researchers’ willingness to conduct an SLR or may lead to search strategies that are too narrow when prioritising time constraints over review quality. Moreover, it may render an SLR outdated by the time it is published.

Background and Objective
This systematic literature review addresses model-based cost-effectiveness studies for therapy response monitoring with positron emission tomography (PET) generally combined with low-dose computed tomography (CT) for various cancer types. Given the known heterogeneity in therapy response events, studies should consider patient-level modelling rather than cohort-based modelling because of its flexibility in handling these events and the time to events. This review aims to identify the modelling methods used and includes a systematic assessment of the assumptions made in the current literature.

Methods
This study was conducted and reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 statement. Information sources included electronic bibliographic databases, reference lists of review articles and contact with experts in the fields of nuclear medicine, health technology assessment and health economics. Eligibility criteria included peer-reviewed scientific publications and published grey literature. Literature searches, screening and critical appraisal were conducted by two reviewers independently. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS) were used to assess the methodological quality. The Bias in Economic Evaluation (ECOBIAS) checklist was used to determine the risk of bias in the included publications.

Results
The search results included 2959 publications. The number of publications included for data extraction and synthesis was ten, representing eight unique studies. These studies addressed patients with lymphoma, advanced head and neck cancers, brain tumours, non-small cell lung cancer and cervical cancer. All studies addressed response to chemotherapy. No study evaluated response to immunotherapy. Most studies positioned PET/CT as an add-on modality and one study positioned PET/CT as a replacement for conventional imaging (X-ray and contrast-enhanced CT). Three studies reported decision-tree structures, four studies reported cohort-level state-transition models and one study reported a partitioned survival model. No patient-level models were reported. The simulation horizons adopted ranged from 1 year to lifetime. Most studies reported a probabilistic analysis, whereas two studies reported a deterministic analysis only. Two studies conducted a value of information analysis. Multiple studies did not adequately discuss model-specific aspects of bias. Most importantly and regularly observed were a high risk of structural assumptions bias, limited simulation horizon bias and wrong model bias.

Conclusions
Model-based cost-effectiveness analysis for therapy response monitoring with PET/CT was based on cohorts of patients instead of individual patients in the current literature. Therefore, the heterogeneity in therapy response events was commonly not addressed appropriately. Further research should include more advanced and patient-level modelling approaches to accurately represent the complex context of clinical practice and, therefore, to be meaningful to support decision making.

Registration
This review is registered in PROSPERO, the international prospective register of systematic reviews funded by the National Institute for Health Research, with CRD42023402581.

Objectives: Health economic (HE) models are often considered as “black boxes” because they are not publicly available and lack transparency, which prevents independent scrutiny of HE models. Additionally, validation efforts and validation status of HE models are not systematically reported. Methods to validate HE models in absence of their full underlying code are therefore urgently needed to improve health policy making. This study aimed to develop and test a generic dashboard to systematically explore the workings of HE models and validate their model parameters and outcomes. Methods: The Probabilistic Analysis Check dashBOARD (PACBOARD) was developed using insights from literature, health economists, and a data scientist. Functionalities of PACBOARD are (1) exploring and validating model parameters and outcomes using standardized validation tests and interactive plots, (2) visualizing and investigating the relationship between model parameters and outcomes using metamodeling, and (3) predicting HE outcomes using the fitted metamodel. To test PACBOARD, 2 mock HE models were developed, and errors were introduced in these models, eg, negative costs inputs, utility values exceeding 1. PACBOARD metamodeling predictions of incremental net monetary benefit were validated against the original model's outcomes. Results: PACBOARD automatically identified all errors introduced in the erroneous HE models. Metamodel predictions were accurate compared with the original model outcomes. Conclusions: PACBOARD is a unique dashboard aiming at improving the feasibility and transparency of validation efforts of HE models. PACBOARD allows users to explore the working of HE models using metamodeling based on HE models’ parameters and outcomes.