PharmacoEconomics最新文献

Background and objective: The reliability of a decision model to guide decision making depends on its ability to accurately predict patient outcomes. We present results of an external validation of the MicroSimulation Core Obesity Model (MS-COM) that was developed to compare the cost effectiveness of obesity management interventions in adults.

Methods: We updated a 2018 systematic literature review of economic models in overweight and obesity and conducted additional targeted searches to identify suitable sources and outcomes to validate against MS-COM in people with overweight or obesity with or without type 2 diabetes. We extracted baseline characteristics and cardiovascular and mortality outcomes, where these were closely matched with MS-COM, and incidence of type 2 diabetes. We performed external-dependent (sources used in MS-COM) and external-independent (sources not used in MS-COM) validation. The extent of concordance between predicted and observed outcomes was assessed using the coefficient of determination (R²), ordinary least-squares linear regression line (OLS LRL), mean absolute percentage error, root mean square percentage error and mean squared log of accuracy ratio.

Results: Ninety-nine potential independent validation sources were identified from 6381 screened records, of which nine studies reported cardiovascular and mortality outcomes that were closely matched with MS-COM, along with two studies that reported type 2 diabetes incidence (number of endpoints = 106). The dependent validation of cardiovascular and mortality outcomes (N = 18), based on the QRisk3 risk equation (normoglycaemia/prediabetes population) and UKPDS 82 (type 2 diabetes population), showed a good linear correlation with observed outcomes (R² = 0.99 and 0.98, respectively). There was some slight overprediction of QRisk3 (OLS LRL slope = 1.11) and underprediction of UKPDS 82 (OLS LRL slope = 0.97). The independent validation of cardiovascular and mortality outcomes also showed a good linear correlation with observed outcomes, particularly in adults with normoglycaemia/prediabetes (R² = 0.90; OLS LRL slope = 0.86); however, an independent validation of type 2 diabetes incidence showed a poorer fit with some degree of underprediction (R² = 0.74; OLS LRL slope = 0.66). Mean error estimates were lower in the dependent validation, showing good concordance between predicted and observed values.

Conclusions: External validation of MS-COM showed good concordance with dependent and independent sources, suggesting the model accurately predicts obesity-related complications in an overweight/obese population with normoglycaemia/prediabetes and type 2 diabetes.

Objectives: Obstructive hypertrophic cardiomyopathy (oHCM) is a myocardial disease, characterised by left ventricular hypertrophy, hampering the ventricular blood outflow. Standard of care (SoC) includes medications such as beta-blockers (BB) and calcium channel blockers (CCB) and septal reduction therapies. Recently, mavacamten, a first-in-class myosin inhibitor, became available to oHCM patients. The objective was to develop a decision analytic model to evaluate the cost effectiveness of mavacamten compared with SoC in oHCM patients from a Dutch societal perspective.

Methods: A Markov model was developed in R based on the Decision Analysis in R for Technologies in Health framework with data from the EXPLORER-HCM trial. This trial compared mavacamten in combination with background therapy (BB and CCB) versus placebo, including oHCM patients (n = 251; mean age 59 years) in New York Heart Association (NYHA) functional classes II (72.9%) and III (27.1%). For the model, four health states were defined based on the NYHA classes, including NYHA I-NYHA III/IV and death. The model evaluated mavacamten with SoC versus SoC alone over a lifetime horizon with a cycle length of 4 weeks, following the most recent Dutch guidelines. Health state utilities and societal costs were derived from the AFFECT-HCM study, with utilities measured using the EQ-5D-5L. Outcomes included (incremental) societal costs, life years (LYs), quality-adjusted life years (QALYs) and the incremental cost-effectiveness ratio (ICER). The Dutch willingness-to-pay thresholds of €50,000 and €80,000 per QALY were applied. Uncertainty of parameters was assessed in deterministic and probabilistic sensitivity and scenario analyses.

Results: Results indicate mavacamten being more effective (Δ4.75 LYs; Δ3.36 QALYs) and more costly (Δ€235,951) compared with SoC with an ICER of €70,223 per QALY gained. Varying parameters by 20% showed that the utility value of patients in NYHA class I (ICER: €57,199; €111,506 per QALY) and drug costs (ICER: €53,985; €86,555 per QALY) were most sensitive. Mavacamten accumulated most LYs, QALYs and costs by patients improving to NYHA class I, compared with SoC, and patients remained longer in that state throughout the model. For men, incremental QALYs (Δ 3.36) and costs (Δ €239,743) were slightly higher compared with women. The probability of the intervention being cost effective at the willingness-to-pay thresholds €50,000 and €80,000 per QALY was 1.3% and 87.4%, respectively. Conclusion The results show that mavacamten increased LYs and QALYs compared with SoC, however, at substantial additional costs. The probability of mavacamten being cost effective depends on the selected willingness-to-pay threshold.

Patient and public involvement (PPI) in health economics modelling is increasingly recommended, yet formal guidance for how to structure or evaluate it remains limited. The Values in Modelling (VIM) framework was developed to address this gap by helping teams identify and deliberate on value-laden decisions in modelling. Drawing on philosophical theory, the framework defines five steps to guide collaboration between modellers and transdisciplinary participators and to document their influence on decision making: (1) identify ethical issues and perspectives; (2) characterize modelling decisions; (3) select decision-making strategies; (4) deliberate 'open' decisions; and (5) report and evaluate. We applied the VIM framework in the Lifetime Exposures and Asthma Outcomes Projection (LEAP) model project, which models the cost effectiveness of high-efficiency particulate air (HEPA) filters for asthma prevention and management. In this application, the framework helped prioritize modelling decisions for PPI, supported transparent deliberation about uncertainty, and led to concrete methodological changes-including new sensitivity analyses and revised outcome measures. These results demonstrate how a theory-informed process can enhance PPI in modelling, improving transparency, justification, and adequacy-for-purpose in health economics research.

Background: Children with multidrug-resistant (MDR)/rifampicin-resistant (RR) tuberculosis (TB) are an important but neglected group in cost-effectiveness research. Digital health information systems enable new approaches to health-service cost analysis. The Provincial Health Data Centre (PHDC) in the Western Cape, South Africa, collates disparate health system data including hospital inpatient and outpatient data, medications, laboratory tests, and primary health care utilisation.

Methods: A health-service cost analysis used anonymised, integrated PHDC data for children treated for MDR/RR-TB between 2018 and 2021. Health-service utilisation was costed using local unit prices, and total per-patient costs were summarised by key patient and disease characteristics (age, sex, resistance profile, site of disease, and HIV status) and reported in 2021 USD. A log-linear regression model identified cost drivers, and alternative parametric distributions were fitted to total costs to assess distributional fit.

Results: There was significant total cost variation across the 271 children in the data sample (median US$7576; interquartile range 2725-22,986). Regression analysis indicates younger age, extrapulmonary disease site, living with HIV, and treatment duration had significant impact on costs; impact of resistance profile was significant but subject to modelling assumptions. The distribution of total per-patient costs fitted a gamma distribution (α = 0.93, β = 14,496).

Conclusion: Treatment for MDR/RR-TB in children remains costly for health systems. Utilising routinely collected, real-world data from an established health information system enables accurate and representative insights to overall costs and major cost drivers. Costs were highly skewed, with a small proportion of patients incurring very high costs. This cost analysis can assist in decision making and programme development at local and international levels and as an input to secondary analysis.

Background and objective: Efanesoctocog alfa is a first-in-class high-sustained factor VIII therapy approved for prophylaxis, on-demand treatment, and peri-operative management of bleeding in hemophilia A. This analysis aimed to compare the cost effectiveness of efanesoctocog alfa prophylaxis with factor VIII extended half-life prophylaxis.

Methods: A lifetime Markov model was developed from a US payer perspective, using clinical data from an indirect treatment comparison of phase III studies and inputs derived from the literature. A cohort of patients aged ≥ 12 years with severe hemophilia A without inhibitors, who received prophylaxis using any regimen or on-demand treatment, entered the model. Outcomes included joint and non-joint bleeds, quality-adjusted life-years, total direct costs, and the incremental cost-effectiveness ratio. Costs were expressed in US dollars and inflated to January 2023 prices. Discount rates of 3% were used. One-way probabilistic and scenario analyses were conducted. The willingness-to-pay threshold was assumed at $150,000 per quality-adjusted life-year.

Results: Efanesoctocog alfa was more effective and less costly (also referred to as 'dominant') versus factor VIII extended half-life therapies, with a lower lifetime number of joint (undiscounted 34.00 vs 140.65) and non-joint (undiscounted 13.33 vs 55.99) bleeds, higher quality-adjusted life-years (24.00 vs 22.92), and lower total costs ($30,716,640 vs $32,953,485). A broad range of scenario analyses and probabilistic sensitivity analyses resulted in 100% of simulations being cost effective. Dosing level and drug costs had the largest impact on results in the deterministic sensitivity analyses.

Conclusions: Our analysis suggests that efanesoctocog alfa was dominant versus prophylaxis with standard and elevated factor VIII extended half-life dosing regimens. Efanesoctocog alfa was associated with better joint health and, hence, contributed to fewer bleeds, lower costs, and higher quality-adjusted life-years.

Simulation models inform health policy decisions by integrating data from multiple sources and forecasting outcomes when there is a lack of comprehensive evidence from empirical studies. Such models have long supported health policy for cancer, the first or second leading cause of death in over 100 countries. Discrete-event simulation (DES) and Bayesian calibration have gained traction in the field of decision science because they enable flexible modeling of complex health conditions and produce estimates of model parameters that reflect real-world disease epidemiology and data uncertainty given model constraints. This uncertainty is then propagated to model-generated outputs, enabling decision-makers to assess confidence in recommendations and estimate the value of collecting additional information. However, there is limited end-to-end guidance on structuring a DES model for cancer progression, estimating its parameters using Bayesian calibration, and applying the calibration outputs to policy evaluation. To fill this gap, we introduce the DES Modeling Framework for Cancer Interventions and Population Health in R (DESCIPHR), an open-source codebase integrating a flexible DES model for the natural history of cancer, Bayesian calibration for parameter estimation, and an example application of screening strategy evaluation. To illustrate the framework, we apply DESCIPHR to calibrate bladder and colorectal cancer models to real-world cancer registry targets. We also introduce an automated method for generating data-informed parameter prior distributions and increase the functionality of a neural network emulator-based Bayesian calibration algorithm. We anticipate that the adaptable DESCIPHR modeling template will facilitate the construction of future decision models evaluating the risks and benefits of health interventions.

Combination vaccines combine several components in a single dose administration. They offer programmatic and public health advantages, particularly as vaccine schedules become increasingly crowded. They are often more expensive to develop and produce, which discourages manufacturer investment without clear market signals. Hence their benefits need to be captured with existing health economic evaluation reference cases used by decision-makers to guide vaccine investments. We propose that the value of combination vaccines can be captured through at least four domains: (1) reductions in tangible and intangible costs to caregivers, (2) operational efficiencies to the health system, (3) opportunity costs of vaccine schedule slots, and (4) more streamlined vaccine schedules. We demonstrate the practicality of our framework by comparing the value of introducing a hypothetical vaccine to a crowded schedule as a standalone formulation, a replacement for a vaccine already in the schedule, or a combination product. The framework could also be applied to estimate the value of reducing the number of separate administrations needed for a standalone vaccine. Applying it in real-world situations could be facilitated by further data collection, particularly on collating results on the value of existing vaccines in the schedule and estimating willingness-to-pay for fewer vaccine administrations.

Background: Individual-level microsimulation models are essential for evaluating colorectal cancer (CRC) screening programmes to capture the heterogeneity in disease progression. To ensure regional relevance, such models require detailed natural history structures and robust calibration to population-specific data. This study presents the development of the first CRC natural history microsimulation model tailored to Northern Ireland (NI) for evaluating the NI Bowel Cancer Screening Programme (NI BCSP).

Method: The model simulates individual trajectories from adenoma onset to CRC diagnosis. Eight natural history parameters were calibrated to sex-specific CRC incidence data, initially using empirical (frequentist) calibration and Approximate Bayesian Computation (ABC) rejection, followed by the ABC-Markov Chain Monte Carlo (ABC-MCMC) algorithm. Other parameters were informed by NI-specific data sources.

Results: The frequentist and ABC rejection calibration approach's posterior distributions informed the prior distribution for the ABC-MCMC approach. ABC-MCMC was informative, yielding 55 parameter sets, but results were constrained by limited calibration targets and parameter identifiability.

Conclusion: This is the first NI-specific CRC microsimulation model, providing a regionally tailored platform for evaluating screening strategies and supporting policy. Calibration was feasible in a data-limited context, but further refinement and additional targets are needed to improve parameter estimation.