PharmacoEconomics最新文献

Objective: We aimed to facilitate the comparison and communication of magnitudes of health inequality impact across interventions for different diseases, and to indicate the potential range of such impacts.

Methods: We propose rescaling the slope index of inequality to measure the health inequality impact as the change in the gap in total predicted quality-adjusted life-years between the least and most socially disadvantaged groups, with linear regression predictions used to account for effects on intermediate groups. We suggest reporting the inequality impact relative to the total health opportunity cost to facilitate comparison across interventions varying in scale and unit costs. We illustrated the approach with aggregate distributional cost-effectiveness analyses of hypothetical treatments for 1336 diseases in England. We approximated benefit shares for neighbourhood deprivation quintile groups using disease-specific hospital admissions. We tested between-group equality using generalised linear regression and constructed uncertainty intervals using Monte Carlo simulation. We assumed an equal total health opportunity cost and benefit-cost ratio of one, with alternative scenarios in a sensitivity analysis.

Results: Health inequality impacts of hypothetical treatments ranged from - 33.1% of the total health opportunity cost (inequality increasing) to + 45.3% (inequality decreasing), and were ≤ - 5% for 1.6% of diseases, ≥ + 5% for 41.8% and ≥ + 20% for 1.6%. The impact was positively associated with the benefit-cost ratio and decreased when more deprived groups were assumed to incur proportionately more total health opportunity costs.

Conclusions: Health inequality impacts can be compared using the change in the total predicted quality-adjusted life-year gap between the least and most socially disadvantaged groups as a proportion of the total health opportunity cost.

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.

Background and objective: A primary elective total knee replacement is routinely used for patients with advanced osteoarthritis. Knee implants differ in characteristics (constraint, fixation, mobility), costs, need for revisions and other health outcomes, and so models evaluating their relative cost effectiveness are required to optimise decision making. Economic modelling approaches differ in complexity, the simplest in use being discrete time Markov models (DTMMs). Continuous-time Markov models (CTMMs) can capture transition timing in finer detail, and can more flexibly relax the constant hazard assumption. Multistate microsimulation can more easily capture patient history and time dependence. This paper aims to explore how the choice of modelling approach influences the cost effectiveness of various implant types for a total knee replacement. Based on the frequency of implant use in the National Joint Registry, 12 commonly used implants were included in the analysis.

Methods: We compared four different models of increasing complexity for male and female individuals in five age categories undergoing a total knee replacement. The DTMM and constant hazard CTMM assumed fixed revision probabilities over time. The individual-level CTMM with splines were semi-Markov, allowing time-varying rates of first revision surgery. The multistate microsimulation incorporated time-dependent splines for all revision rates but also dependence on time spent in previous health states. All revision rates were estimated using data from the National Joint Registry. The models were implemented using the hesim package in R.

Results: Under the constant hazard assumption, DTMM and CTMM yielded similar results, identifying the most commonly used implant as the most cost effective. However, using the spline-based hazard CTMM and patient history informed multistate microsimulation, other implants were identified as the most cost-effective options. The increased model complexity required high-performance computing facilities for CTMMs and multistate microsimulation.

Conclusions: This study shows that the choice of model can impact cost-effectiveness results. The multistate microsimulation model, which incorporates time-dependent transitions, provides a realistic representation of patient pathways over time, but is computationally complex and may be preferable only when time-varying risks are a key factor. The CTMM or DTMM models may be more efficient when data are limited or computational resources are constrained. Improving the accuracy and applicability of economic models can improve healthcare decision making. Future research should extend these methodologies to other disease areas, refine continuous-time models and explore their impact across diverse healthcare contexts.

Background: Cure models are becoming more popular for modelling survival data where long-term survival, or 'cure', is considered plausible. One criterion for considering fitting cure models is evidence for a plateau in the Kaplan-Meier survival curve. However, what constitutes a mathematical definition of a plateau in survival probability is unclear, and visual inspections of survival curves are subjective.

Objective: We investigate these issues and clarify what is meant by a plateau in this context.

Methods: We begin by describing an activity where five experts were presented with 10 survival curves from oncology trials. They were asked to rank these curves in order of their potential suitability for mixture cure modelling. We explore mathematically what features of data are required to produce a positive estimated cure fraction under an exponential mixture cure model. We show how these results can be generalised to a Weibull mixture cure model. A case study was performed using one of the survival curves.

Results: We found weak correlations between the experts' rankings. Mathematical investigations revealed the features of data required for mixture cure models to be potentially useful, such as a decreasing event rate, but this is highly model dependent. The case study illustrated similar statistical issues.

Conclusions: We conclude that a precise definition of the extent to which a Kaplan-Meier survival curve demonstrates a plateau is likely to prove elusive. External evidence or subject matter expert knowledge about the plausibility of cure must therefore play a key role.

Background and objective: Health technology assessments traditionally rely on clinical trial data, leaving uncertainties about real-world cost effectiveness. This post-market economic evaluation used registry data to estimate the real-world cost effectiveness of bortezomib, lenalidomide and dexamethasone (VRd) versus standard of care as it existed prior to VRd funding for newly diagnosed, transplant eligible and ineligible multiple myeloma, as subsidised by the Australian government in 2019.

Methods: We conducted the economic evaluation from the perspective of the Australian healthcare system using the EpiMAP Myeloma model, a discrete event simulation model powered by risk equations based on data from the Australia & New Zealand Myeloma and Related Diseases Registry. This approach captured individual patient heterogeneity and treatment pathways through up to nine lines of therapy. We assessed differences in quality-adjusted life-years and costs over a lifetime horizon, discounting both at the standard Australian rate of 5% per annum. Costs were valued in 2025 Australian dollars and non-parametric bootstrapping was used to quantify parameter uncertainty.

Results: Brtezomib, lenalidomide and dexamethasone was associated with 0.16 incremental quality-adjusted life-years (95% confidence interval [CI] 0.10, 0.21) and A$16K incremental costs (95% CI A$12K, A$120K). Improved response to therapy with VRd was predicted to marginally increase receipt of autologous stem cell transplantation by 1.1% (95% CI 0.6, 1.7), significantly increase receipt of maintenance therapy by 13.8% (95% CI 10.4, 17.3) and marginally decrease the proportion of patients progressing to subsequent lines. None of the bootstrap iterations fell below the traditional A$50K/quality-adjusted life-year threshold.

Conclusions: The 2019 decision to universally fund VRd for newly diagnosed multiple myeloma did not result in a cost-effective allocation of healthcare resources when judged against the traditional A$50K/quality-adjusted life-year threshold. Our findings provide nuanced insights into the real-world cost effectiveness of VRd, highlighting how post-market evaluations can inform refinement of funding decisions for complex therapeutic interventions.