Health Economic Model Research Articles

Using health evidence to influence healthier urban development: a qualitative evaluation of a researcher–practitioner collaboration

ABSTRACT Despite evidence showing health impacts associated with environmental features such as housing, air quality, transport, and greenspaces, urban development decisions often result in less healthy environments. To address research-practice gaps we explore a collaboration involving an embedded researcher who bridged between urban development practitioners and researchers on a UK local government-led regeneration project. This facilitated development and implementation of a new health economic model that demonstrated the health impacts of changes to the built environment. We shared evidence at multiple timepoints to influence decision-making for a regeneration framework. Evaluation of this approach involved analysis of semi-structured interviews with key practitioners, alongside project meeting notes and embedded researcher field notes. We found that the academic-practitioner partnership, enabled by the embedded researcher, helped academics understand a complex system, and provided contextually relevant evidence for practitioners to highlight problems; support good/aspirational solutions; and consider trade-offs. Academic-practitioner collaborations can help develop and implement impactful interventions to tackle important, and complex, health challenges associated with the built environment. Understanding how evidence may be useful for different purposes can help to implement its use more effectively, and translate it for different audiences.

The PHEM-B toolbox of methods for incorporating the influences on Behaviour into Public Health Economic Models

BackgroundIt is challenging to predict long-term outcomes of interventions without understanding how they work. Health economic models of public health interventions often do not incorporate the many determinants of individual and population behaviours that influence long term effectiveness. The aim of this paper is to draw on psychology, sociology, behavioural economics, complexity science and health economics to: (a) develop a toolbox of methods for incorporating the influences on behaviour into public health economic models (PHEM-B); and (b) set out a research agenda for health economic modellers and behavioural/ social scientists to further advance methods to better inform public health policy decisions.MethodsA core multidisciplinary group developed a preliminary toolbox from a published review of the literature and tested this conceptually using a case study of a diabetes prevention simulation. The core group was augmented by a much wider group that covered a broader range of multidisciplinary expertise. We used a consensus method to gain agreement of the PHEM-B toolbox. This included a one-day workshop and subsequent reviews of the toolbox.ResultsThe PHEM-B toolbox sets out 12 methods which can be used in different combinations to incorporate influences on behaviours into public health economic models: collaborations between modellers and behavioural scientists, literature reviewing, application of the Behaviour Change Intervention Ontology, systems mapping, agent-based modelling, differential equation modelling, social network analysis, geographical information systems, discrete event simulation, theory-informed statistical and econometric analyses, expert elicitation, and qualitative research/process tracing. For each method, we provide a description with key references, an expert consensus on the circumstances when they could be used, and the resources required.ConclusionsThis is the first attempt to rigorously and coherently propose methods to incorporate the influences on behaviour into health economic models of public health interventions. It may not always be feasible or necessary to model the influences on behaviour explicitly, but it is essential to develop an understanding of the key influences. Changing behaviour and maintaining that behaviour change could have different influences; thus, there could be benefits in modelling these separately. Future research is needed to develop, collaboratively with behavioural scientists, a suite of more robust health economic models of health-related behaviours, reported transparently, including coding, which would allow model reuse and adaptation.

Including carer health-related quality of life in NICE health technology assessments in the United Kingdom.

The impact of health technologies may extend beyond the patient and affect the health of people in their network, like their informal carers. The National Institute for Health and Care Excellence (NICE) methods guide explicitly allows the inclusion of health-related quality of life (HRQoL) effects on carers in economic evaluations when these effects are substantial, but the proportion of NICE appraisals that includes carer HRQoL remains small. This paper discusses when inclusion of carer HRQoL is justified, how inclusion can be substantiated, and how carer HRQoL can be measured and included in health economic models. Inclusion of HRQoL in economic evaluations can best be substantiated by data collected in (carers for) patients eligible for receiving the intervention. To facilitate combining patient and carer utilities on the benefit side of economic evaluations, using EQ-5D to measure impacts on carers seems the most successful strategy in the UK context. Alternatives to primary data collection of EQ-5D include vignette studies, using existing values, and mapping algorithms. Carer HRQoL was most often incorporated in economic models in NICE appraisals by employing (dis)utilities as a function of the patient's health state or disease severity. For consistency and comparability, economic evaluations including carer HRQoL should present analyses with and without carer HRQoL.

Exploring the potential cost-effectiveness of a new computerised decision support tool for identifying fetal compromise during monitored term labours: an early health economic model

BackgroundAround 60% of term labours in the UK are continuously monitored using cardiotocography (CTG) to guide clinical labour management. Interpreting the CTG trace is challenging, leading to some babies suffering adverse outcomes and others unnecessary expedited deliveries. A new data driven computerised tool combining multiple clinical risk factors with CTG data (attentive CTG) was developed to help identify term babies at risk of severe compromise during labour. This paper presents an early health economic model exploring its potential cost-effectiveness.MethodsThe model compared attentive CTG and usual care with usual care alone and simulated clinical events, healthcare costs, and infant quality-adjusted life years over 18 years. It was populated using data from a cohort of term pregnancies, the literature, and administrative datasets. Attentive CTG effectiveness was projected through improved monitoring sensitivity/specificity and potential reductions in numbers of severely compromised infants. Scenario analyses explored the impact of including litigation costs.ResultsNationally, attentive CTG could potentially avoid 10,000 unnecessary alerts in labour and 2400 emergency C-section deliveries through improved specificity. A reduction of 21 intrapartum stillbirths amongst severely compromised infants was also predicted with improved sensitivity. Attentive CTG could potentially lead to cost savings and health gains with a probability of being cost-effective at £25,000 per QALY ranging from 70 to 95%. Potential exists for further cost savings if litigation costs are included.ConclusionsAttentive CTG could offer a cost-effective use of healthcare resources. Prospective patient-level studies are needed to formally evaluate its effectiveness and economic impact in routine clinical practice.

IPECAD Modeling Workshop 2023 Cross Comparison Challenge on Cost-Effectiveness Models in Alzheimer’s Disease

Decision-analytic models assessing the value of emerging Alzheimer's disease (AD) treatments are challenged by limited evidence on short-term trial outcomes and uncertainty in extrapolating long-term patient-relevant outcomes. To improve understanding and foster transparency and credibility in modeling methods, we cross-compared AD decision models in a hypothetical context of disease-modifying treatment for mild cognitive impairment (MCI) due to AD. A benchmark scenario (US setting) was used with target population MCI due to AD and a set of synthetically generated hypothetical trial efficacy estimates. Treatment costs were excluded. Model predictions (10-year horizon) were assessed and discussed during a 2-day workshop. Nine modeling groups provided model predictions. Implementation of treatment effectiveness varied across models based on trial efficacy outcome selection (CDR-SB, CDR-global, MMSE, FAQ) and analysis method (observed severity transitions, change from baseline, progression hazard ratio, or calibration to these). Predicted mean time in MCI ranged from 2.6-5.2 years for control strategy, and from 0.1-1.0 years for difference between intervention and control strategies. Predicted quality-adjusted life-year gains ranged from 0.0-0.6 and incremental costs (excluding treatment costs) from -US$66,897 to US$11,896. Trial data can be implemented in different ways across health-economic models leading to large variation in model predictions. We recommend 1) addressing the choice of outcome measure and treatment effectiveness assumptions in sensitivity analysis, 2) a standardized reporting table for model predictions, and 3) exploring the use of registries for future AD treatments measuring long-term disease progression to reduce uncertainty of extrapolating short-term trial results by health economic models.

Cost-effectiveness of treatment sequences for BRAF-mutant advanced melanoma in the Netherlands using a health economic model

BackgroundThis study aims to evaluate the cost-effectiveness of treatment sequences for patients with advanced melanoma with a BRAF mutation in the Netherlands from a societal perspective. MethodsA semi-Markov model with a life-time horizon has been used to evaluate cost-effectiveness of 21 treatment sequences. Real-world data from the Dutch Melanoma Treatment Registry (DMTR) were used to estimate time to progression, next treatment and death. Utilities by health state as well as hospital costs, health care costs outside the hospital, patient and family costs and productivity costs were also derived from the DMTR. Drug costs were estimated based on the recommended dose and duration of treatment. Incremental cost-effectiveness ratios (ICERs) were presented as incremental costs per QALY gained. ResultsHealth benefits of treatment sequences consisting of targeted therapies and immunotherapies vary between 2.3 and 5.8 QALYs gained per patient when compared to chemotherapy. The increase in costs varies between €112,000 and €383,000. The efficiency frontier consists of nivolumab in the first line followed by ipilimumab in the second line (ICERs of €42,000/QALY and €44,000/QALY), nivolumab in the first line followed by encorafenib plus binimetinib in the second line (ICERs of €71,000/QALY and €68,000/QALY) and nivolumab plus ipilimumab in the first line followed by encorafenib plus binimetinib in the second line (ICERs of €74,000/QALY and €76,000/QALY). The first treatment given within a sequence as well as assumptions regarding treatment duration have a substantial impact on cost-effectiveness outcomes. ConclusionThe ICERs can be considered cost-effective at different cost-effectiveness thresholds.

Strengthening China’s National Essential Public Health Services Package for hypertension and diabetes care: protocol for an interrupted time series study with mixed-methods process evaluation and health economic evaluation

BackgroundDespite major primary health care (PHC) reforms in China with the 2009 launch of the National Essential Public Health Service Package, the country experiences many challenges in improving the management of non-communicable diseases in PHC facilities. “EMERALD” is a multifaceted implementation strategy to strengthen the management of hypertension and type-2 diabetes mellitus (T2DM) in PHC facilities. The study aims to: (1) examine the effectiveness of EMERALD in improving hypertension and T2DM management; (2) evaluate the implementation of the interventions; and (3) use the study findings to model the long-term health economic impact of the interventions.MethodsThe EMERALD intervention components include: (1) empowerment for PHC providers through training and capacity building; (2) empowerment for patient communities through multi-media health education; and (3) empowerment for local health administrators through health data monitoring and strengthening governance of local PHC programs. An interrupted time series design will be used to determine the effectiveness of the interventions based on routinely collected health data extracted from local health information systems. The primary effectiveness outcome is the guideline-recommended treatment rates for people with hypertension and T2DM. Secondary effectiveness outcomes include hypertension and T2DM diagnosis and control rates, and enrolment and adherence rates to the recommended care processes in the National Essential Public Health Service Package. A mixed-methods process evaluation will be conducted to evaluate the implementation of the interventions, including the reach of the target population, adequacy of adoption, level of implementation fidelity, and maintenance. Qualitative interviews with policy makers, health administrators, PHC providers, and patients with hypertension and/or T2DM will be conducted to further identify factors influencing the implementation. In addition, health economic modelling will be performed to explore the long-term incremental costs and benefits of the interventions.DiscussionThis study is expected to generate important evidence on the effectiveness, implementation, and health economic impact of complex PHC interventions to strengthen the primary care sector’s contribution to addressing the growing burden of non-communicable diseases in China.Trial registrationThe study has been registered on Chinese Clinical Trial Registry at https://www.chictr.org.cn/ (Registration number ChiCTR2400082036, on March 19th 2024).

Changes in hand function and health state utility after cubital tunnel release using the United Kingdom Hand Registry.

This study aimed to analyse and contrast changes in health-related quality of life (HR-QoL) and hand symptoms in the first 6 months after surgical treatment for primary cubital tunnel syndrome. Data originated from the United Kingdom Hand Registry. HR-QoL was assessed using the generic EuroQol five-dimensional assessment tool (EQ-5D-5L) and hand symptoms using the Patient Evaluation Measure (PEM). In total, 281 patients were included in the statistical analysis. Cubital tunnel release resulted in clinically relevant relief of hand symptoms. However, no improvement in HR-QoL was detected by the EQ-5D-5L. As a result, current health economic models, such as those used by the National Institute for Health Care Excellence (NICE) in the UK, might conclude that cubital tunnel release is not cost-effective. This discrepancy requires exploration, and hand-specific preference-based measures might be needed for value-based healthcare in hand surgery.Level of evidence: III.

General Population Mortality Adjustment in Survival Extrapolation of Cancer Trials: Exploring Plausibility and Implications for Cost-Effectiveness Analyses in HER2-Positive Breast Cancer in Sweden.

In economic evaluations of novel therapies, assessing lifetime effects based on trial data often necessitates survival extrapolation, with the choice of model affecting outcomes. The aim of this study was to assess accuracy and variability between alternative approaches to survival extrapolation. Data on HER2-positive breast cancer patients from the Swedish National Breast Cancer Register were used to fit standard parametric distribution (SPD) models and excess hazard (EH) models adjusting the survival projections based on general population mortality (GPM). Models were fitted using 6-y data for stage I and II, 4-y data for stage III, and 2-y data for stage IV cancer reflecting an early data cutoff while maintaining sufficient events for comparison of model estimates with actual long-term outcomes. We compared model projections of 15-y survival and restricted mean survival time (RMST) to 15-y registry data and explored the variability between models in extrapolations of long-term survival. Among 11,224 patients compared with the observed registry 15-y RMST estimates across the disease stages, EH cure models provided the most accurate estimates in patients with stage I to III cancer, whereas EH models without cure most closely matched survival in patients with stage IV cancer, in which cure assumption was less plausible. The Akaike information criterion-averaged model projections varied as follows: -8.2% to +5.3% for SPD models, -4.9% to +5.2% for the EH model without a cure assumption, and -19.3% to -0.2% for the EH model with a cure assumption. EH models significantly reduced between-model variance in the predicted RMSTs over a 50-y time horizon compared with SPD models. EH models may be considered as alternatives to SPD models to produce more accurate and plausible survival extrapolation that accounts for general population mortality. Excess hazard (EH) methods have been suggested as an approach to incorporate background mortality rates in economic evaluation using survival extrapolation.We highlight that EH models with or without a cure assumption can produce more accurate survival projections and significantly reduce between-model variability in comparison with standard parametric distribution models across cancer stages.EH models may be a preferred modeling method to reduce model uncertainty in health economic modeling since models that would otherwise have produced implausible extrapolations are constrained by the EH framework.Reduced uncertainty in economic evaluations will enhance the application of evidence-based health care decision making.

‘Managing values’ in health economics modelling: Philosophical and practical considerations

Stakeholder involvement has been proposed as a key strategy for appropriately managing value-laden decisions or ‘value judgments’ in health economics modelling. Philosophers of science, however, conceive of stakeholder involvement in research in conflicting ways, and also propose alternative strategies for ‘managing values’ in science. Furthermore, all proposed strategies for managing values in science raise philosophical questions and practical challenges that are difficult to resolve. As a result, health economists who seek to appropriately inform value judgments in modelling must currently go without straightforward guidance. There is a need to further explore how health economists should manage value judgments in modelling, taking into account philosophical debates and contextual constraints. This paper discusses core proposals for managing values in science and identifies philosophical questions and practical challenges these proposals leave unresolved. It further considers how this could potentially inform processes to manage value judgments in health economics modelling, using examples from an ongoing modelling project called LEAP (Lifetime Exposures and Asthma Outcomes Projection). We conclude that all strategies to ‘manage values’ in health economics modelling have strengths and weaknesses, but are generally compatible with one another, suggesting that health economists may use a combination of strategies. Further research is needed to explore the effects of strategies to ‘manage values’ in health economics modelling.

Empirical Testing of Alternative Search Methods to Retrieve Utility Values for Health Economic Modelling.

The objective of this study is to compare different information retrieval methods that can be used to identify utility inputs for health economic models. Theusual practice of using systematic review methods was compared with two alternatives (iterative searching and rapid review), using a health technology assessment (HTA) case study in ulcerative colitis (UC). We analysed whether there were differences in the utility values identified when using the alternative search methods. Success was evaluated in terms of time, burden and relevance of identified information. The identified utility values were tested in an executable health economic model developed for UC, and the model results were compared. The usual practice of using systematic review search approaches identified the most publications but was also the least precise method and took longest to complete.The inclusion of data from the different search methods in the model did not lead to different conclusions across search methods. In this case study, usual practice was less efficient and resulted in the same health economic model conclusions as the alternative search methods. Further case studies are required to examine whether this conclusion might be generalisable.

RWE ready for reimbursement? A round up of developments in real-world evidence relating to health technology assessment: part 16.

In this update, we discuss recent US FDA guidance offering more specific guidelines on appropriate study design and analysis to support causal inference for non-interventional studies and the launch of the European Medicines Agency(EMA) and the Heads of Medicines Agencies (HMA) public electronic catalogues. We also highlight an article recommending assessing data quality and suitability prior to protocol finalization and a Journal of the American Medical Association-endorsed framework for using causal language when publishing real-world evidence studies. Finally, we explore the potential of large language models to automate the development of health economic models.

Accurate EVSI Estimation for Nonlinear Models Using the Gaussian Approximation Method.

The expected value of sample information (EVSI) measures the expected benefits that could be obtained by collecting additional data. Estimating EVSI using the traditional nested Monte Carlo method is computationally expensive, but the recently developed Gaussian approximation (GA) approach can efficiently estimate EVSI across different sample sizes. However, the conventional GA may result in biased EVSI estimates if the decision models are highly nonlinear. This bias may lead to suboptimal study designs when GA is used to optimize the value of different studies. Therefore, we extend the conventional GA approach to improve its performance for nonlinear decision models. Our method provides accurate EVSI estimates by approximating the conditional expectation of the benefit based on 2 steps. First, a Taylor series approximation is applied to estimate the conditional expectation of the benefit as a function of the conditional moments of the parameters of interest using a spline, which is fitted to the samples of the parameters and the corresponding benefits. Next, the conditional moments of parameters are approximated by the conventional GA and Fisher information. The proposed approach is applied to several data collection exercises involving non-Gaussian parameters and nonlinear decision models. Its performance is compared with the nested Monte Carlo method, the conventional GA approach, and the nonparametric regression-based method for EVSI calculation. The proposed approach provides accurate EVSI estimates across different sample sizes when the parameters of interest are non-Gaussian and the decision models are nonlinear. The computational cost of the proposed method is similar to that of other novel methods. The proposed approach can estimate EVSI across sample sizes accurately and efficiently, which may support researchers in determining an economically optimal study design using EVSI. The Gaussian approximation method efficiently estimates the expected value of sample information (EVSI) for clinical trials with varying sample sizes, but it may introduce bias when health economic models have a nonlinear structure.We introduce the spline-based Taylor series approximation method and combine it with the original Gaussian approximation to correct the nonlinearity-induced bias in EVSI estimation.Our approach can provide more precise EVSI estimates for complex decision models without sacrificing computational efficiency, which can enhance the resource allocation strategies from the cost-effective perspective.

Assessing economic investment required to scale up bariatric surgery capacity in England: a health economic modelling analysis

ObjectivesTo quantify the economic investment required to increase bariatric surgery (BaS) capacity in National Health Service (NHS) England considering the growing obesity prevalence and low provision of BaS in England...

Examining the Impact of Structural Uncertainty Across 10 Type 2 Diabetes Models: Results From the 2022 Mount Hood Challenge

ObjectivesThe Mount Hood Diabetes Challenge Network aimed to examine the impact of model structural uncertainty on the estimated cost-effectiveness of interventions for type 2 diabetes. MethodsTen independent modeling groups completed a blinded simulation exercise to estimate the cost-effectiveness of 3 interventions in 2 type 2 diabetes populations. Modeling groups were provided with a common baseline population, cost and utility values associated with different model health states, and instructions regarding time horizon and discounting. We collated the results to identify variation in predictions of net monetary benefit (NMB) and the drivers of those differences. ResultsOverall, modeling groups agreed which interventions had a positive NMB (ie, were cost-effective), Although estimates of NMB varied substantially—by up to £23 696 for 1 intervention. Variation was mainly driven through differences in risk equations for complications of diabetes and their implementation between models. The number of modeled health states was also a significant predictor of NMB. ConclusionsThis exercise demonstrates that structural uncertainty between different health economic models affects cost-effectiveness estimates. Although it is reassuring that a decision maker would likely reach similar conclusions on which interventions were cost-effective using most models, the range in numerical estimates generated across different models would nevertheless be important for price-setting negotiations with intervention developers. Minimizing the impact of structural uncertainty on healthcare decision making therefore remains an important priority. Model registries, which record and compare the impact of structural assumptions, offer one potential avenue to improve confidence in the robustness of health economic modeling.

Quantifying societal burden of radiation-induced small bowel toxicity in patients with rectal cancer.

Advancements in rectal cancer (RC) treatment not only led to an increase in lives saved but also improved quality of life (QoL). Notwithstanding these benefits, RC treatment comes at the price of gastrointestinal morbidity in many patients. Health economic modelling poses an opportunity to explore the societal burden of such side-effects. This study aims to quantify radiation-induced late small bowel (SB) toxicity in survivors of RC for Three-Dimensional Conformal Radiation Therapy (3D-CRT), Intensity Modulated Radiation Therapy (IMRT) and Intensity Modulated Radiation Therapy - Image Guided Radiation Therapy (IMRT/IGRT). Materials and A model-based health economic evaluation was performed. The theoretical cohort consists of a case-mix of survivors of RC aged 25-99 years according to Belgian age-specific incidence rates. A societal perspective was adopted. The base case analysis was complemented with one-way deterministic analyses, deterministic scenario analyses and probabilistic sensitivity analysis (1,000 iterations). Results were presented as mean lifetime incremental cost (€) and utility (QALYs) per patient. The analyses showed that the use of innovative radiotherapy (RT) improves lifetime QoL in survivors of RC by 0.11 QALYs and 0.05 QALYs by preferring IMRT/IGRT and IMRT over 3D-CRT, respectively. The use of IMRT/IGRT and IMRT results in an incremental cost-saving of €3,820 and €1,863 per patient, solely by radiation-induced SB toxicity, compared to 3D-CRT. It is important to consider late toxicity effects in decisions regarding investments and reimbursement as our analysis highlighted the potential long-term cost-savings and improved QoL of novel RT techniques in patients with rectal cancer.

A Causal Model for Primary Prevention of Cardiovascular Disease: The Health Economic Model for the Primary Prevention of Cardiovascular Disease

ObjectivesOur objective was to design and develop an open-source model capable of simulating interventions for primary prevention of cardiovascular disease (CVD) that incorporated the cumulative effects of risk factors (eg, cholesterol years or blood-pressure years) to enhance health economic modeling in settings which clinical trials are not possible. MethodsWe reviewed the literature to design the model structure by selecting the most important causal risk factors for CVD—low-density lipoprotein-cholesterol (LDL-C), systolic blood pressure (SBP), smoking, diabetes, and lipoprotein (a) (Lp[a])—and most common CVDs—myocardial infarction and stroke. The epidemiological basis of the model involves the simulation of risk factor trajectories, which are used to modify CVD risk via causal effect estimates derived from Mendelian randomization. LDL-C, SBP, Lp(a), and smoking all have cumulative impacts on CVD risk, which were incorporated into the health economic model. The data for the model were primarily sourced from the UK Biobank study. We calibrated the model using clinical trial data and validated the model against the observed UK Biobank data. Finally, we performed an example health economic analysis to demonstrate the utility of the model. The model is open source. ResultsThe model performed well in all validation tests. It was able to produce interpretable and plausible (consistent with expectations of the existing literature) results from an example health economic analysis. ConclusionsWe have constructed an open-source health economic model capable of incorporating the cumulative effect of LDL-C (ie, cholesterol years), SBP (SBP-years), Lp(a), and smoking on lifetime CVD risk.

Cost Analysis of Prostate Cancer Care Using a Biomarker-enhanced Diagnostic Strategy with Stockholm3

Background and objectiveBuilding on previous research demonstrating better prostate cancer (PC) diagnostics via a biomarker-enhanced approach, this study focuses on cost analysis of PC care using the Stockholm3 test. We assessed the economic impact in European health care systems using real-world evidence for diagnostic outcomes and relevant costs. MethodsWe evaluated two PC diagnostic strategies: (1) the conventional prostate-specific antigen (PSA) strategy with magnetic resonance imaging (MRI) and (2) PSA testing with a reflex to biomarkers at PSA ≥1.5 ng/ml in guiding decisions to perform MRI. Data from the Swedish National Prostate Cancer Register and Capio St. Göran Hospital provided real-world evidence, supplemented by health economic modeling. A comprehensive cost analysis was conducted using a Markov model for treatment pathways for four PC disease states and overall spending, for which costs from various European health care systems were used. A deterministic sensitivity analysis was performed across different cost and diagnostic scenarios. Key finding and limitationsThe average cost for the four disease states was €2 182 for benign disease, €10 023 for low-grade disease, €13 073 for intermediate- to high-grade localized or locally advance disease, and €271 210 for metastatic disease. The overall spending was €358 239 (7.7%) lower per 1000 men tested in the biomarker-enhanced strategy in comparison to the PSA strategy. The primary cost saving was attributed to lower treatment expenses for metastatic disease. Sensitivity analysis affirmed the robustness of the findings across various diagnostic and treatment scenarios. Conclusions and clinical implicationsBiomarker-enhanced diagnostic strategies may reduce health care costs for PC management and are likely to improve quality-adjusted life years in a scenario in which metastatic disease is reduced. Patient summaryWe explored different ways to detect prostate cancer more cost-effectively. We found that using a specific blood test, called Stockholm3, after a PSA (prostate-specific antigen) test to decide if an MRI scan (magnetic resonance imaging) is necessary could save money, mainly by identifying localized cancer earlier and reducing the need for expensive treatments for advanced cancer.

A Systematic Review of Modeling Approaches to Evaluate Treatments for Relapsed Refractory Multiple Myeloma: Critical Review and Considerations for Future Health Economic Models.

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. 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. 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. 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.

P47 GPT: The Next Frontier in Health Economic Modeling?

P47 GPT: The Next Frontier in Health Economic Modeling?