PharmacoEconomics最新文献

Background: Cost-utility analyses (CUAs) increasingly use models to predict long-term outcomes and translate trial data to real-world settings. Model structure uncertainty affects these predictions. This study compares pharmacometric against traditional pharmacoeconomic model evaluations for CUAs of sunitinib in gastrointestinal stromal tumors (GIST).

Methods: A two-arm trial comparing sunitinib 37.5 mg daily with no treatment was simulated using a pharmacometric-based pharmacoeconomic model framework. Overall, four existing models [time-to-event (TTE) and Markov models] were re-estimated to the survival data and linked to logistic regression models describing the toxicity data [neutropenia, thrombocytopenia, hypertension, fatigue, and hand-foot syndrome (HFS)] to create traditional pharmacoeconomic model frameworks. All five frameworks were used to simulate clinical outcomes and sunitinib treatment costs, including a therapeutic drug monitoring (TDM) scenario.

Results: The pharmacometric model framework predicted that sunitinib treatment costs an additional 142,756 euros per quality adjusted life year (QALY) compared with no treatment, with deviations - 21.2% (discrete Markov), - 15.1% (continuous Markov), + 7.2% (TTE Weibull), and + 39.6% (TTE exponential) from the traditional model frameworks. The pharmacometric framework captured the change in toxicity over treatment cycles (e.g., increased HFS incidence until cycle 4 with a decrease thereafter), a pattern not observed in the pharmacoeconomic frameworks (e.g., stable HFS incidence over all treatment cycles). Furthermore, the pharmacoeconomic frameworks excessively forecasted the percentage of patients encountering subtherapeutic concentrations of sunitinib over the course of time (pharmacoeconomic: 24.6% at cycle 2 to 98.7% at cycle 16, versus pharmacometric: 13.7% at cycle 2 to 34.1% at cycle 16).

Conclusions: Model structure significantly influences CUA predictions. The pharmacometric-based model framework more closely represented real-world toxicity trends and drug exposure changes. The relevance of these findings depends on the specific question a CUA seeks to address.

Objectives: This study was designed to test hypotheses regarding the path dependence of health-outcome values in the form of linear additivity of health-state utilities and diminishing marginal utility of health outcomes.

Methods: We employed a discrete-choice experiment to quantify patient treatment preferences for major depressive disorder. In a series of choice questions, participants evaluated seven symptom-improvement sequences and out-of-pocket costs over 6-week durations. Money-equivalent values were derived from a deductive latent-class mixed-logit analysis.

Results: The discrete-choice experiment was completed by 751 respondents with self-reported major depressive disorder recruited from an online commercial panel. The class-membership probability was 0.83 for latent-class preferences consistent with supporting relative importance weights for all symptom-improvement sequences in the study design. First, we found strong support for diminishing marginal utility in symptom-improvement sequences. The money-equivalent value of an initial week of normal mood was $147 (95% confidence interval: $128, $166) and a second week of normal mood was $70 ($49, $91). Furthermore, for short treatment durations where conventional discounting was not a factor, equivalent changes in health status were valued more highly for an earlier onset of effect: holding subsequent symptom patterns constant, $338 (211, 454) versus $70 (49, 91) for improvements starting in week 2 versus week 3 and $147 ($128, $166) versus $29 (-$4, $64) for improvements starting in week 3 versus week 4.

Conclusions: Our findings imply that conventional quality-adjusted life-year calculations in which health values are assumed to be path independent can understate the value of health improvements that appear earlier in a sequence.

Objectives: We conducted a distributional cost-effectiveness analysis to evaluate how coverage of tocilizumab for inpatients with COVID-19 from 2021 to present impacted health equity in the USA.

Methods: A published, payer-perspective, distributional cost-effectiveness analysis for inpatient COVID-19 treatments was adapted to include information on baseline health disparities across 25 equity-relevant subgroups based on race and ethnicity (5 census-based groups), and county-level social vulnerability (5 geographic quintiles). The underlying cost-effectiveness analysis was updated to reflect patient characteristics at admission, standard of care outcomes, tocilizumab efficacy, and contemporary unit costs. The distributional cost-effectiveness analysis inputs for COVID-19 hospitalization and subgroup risk adjustments based on social vulnerability were derived from published estimates. Opportunity costs were estimated by converting total tocilizumab spend into quality-adjusted life-years (QALYs), distributed equally across subgroups.

Results: Tocilizumab treatment was cost effective across all subgroups. Treatment resulted in larger relative QALY gains in more socially vulnerable subgroups than less socially vulnerable subgroups, given higher hospitalization rates and inpatient mortality. Using an opportunity cost threshold of US$150,000/QALY and an Atkinson index of 11, tocilizumab was estimated to have improved social welfare by increasing population health (53,252 QALYs gained) and reducing existing overall US health inequalities by 0.003% since 2021.

Conclusions: Use of tocilizumab for COVID-19 since 2021 increased population health while improving health equity, as more patients with lower baseline health were eligible for treatment and received larger relative health gains. Future equitable access to tocilizumab for inpatients with COVID-19 is expected to lead to continued increases in population health and reductions in disparities.

Current approaches to the pricing and funding of new pharmaceuticals often focus on a one-time decision about a product for each clinical indication. This can result in multiple options being available to health systems without a clear signal about how to prioritise between them. This runs the risk that, as available treatments, evidence, and drug prices evolve, clinical and patient choices may not be aligned with the objective of allocating resources to promote population health. We propose a framework for using cost-effectiveness analysis to support pricing and funding policies for new pharmaceuticals in multi-comparator indications, some of the key aspects of which evolve over time. The framework comprises three core considerations: (1) designing proportionate processes, (2) assessing the costs and benefits of recommending multiple treatment options, and (3) appropriate application of cost-effectiveness analysis 'decision rules' to support recommendations and price negotiations. We highlight that proportionate processes require prioritisation of topics for reassessment to be aligned with clear objectives, the need for full flexibility of decision making at the point of reassessment, and that in some contexts contractual re-specification rather than typical deliberative health technology assessment processes may be more appropriate. We discuss reasons why the recommendation of multiple treatment options rather than a single cost-effective treatment may be appropriate and urge health technology assessment bodies to explicitly address the trade-offs that may be associated with recommending multiple treatments. Finally, we discuss how value-based pricing could be achieved when multiple competitor manufacturers offer confidential discounts.

Objectives: For US Medicare and Medicaid, single drug prices do not reflect the value of supplemental indications. Value-based indication-specific and weighted-average pricing has been suggested for drugs with multiple indications. Under indication-specific pricing, a distinct price is assigned to the differential value a drug offers in each indication. Under weighted-average pricing, a single drug price is calculated that reflects the value and/or volume of each indication. This study estimates price reductions and cost savings for cancer drugs under value-based indication-specific pricing and weighted-average pricing.

Methods: We collected data on US Food and Drug Administration (FDA)-approved cancer drugs and indications (2003-2020) from FDA labels, the Global Burden of Disease study, clinicaltrials.gov, and Medicare and Medicaid. A multivariable regression analysis, informed by characteristics of original indications, was used to predict value-based indication-specific prices for supplemental indications. These indication-specific prices were combined with each indication's prevalence data to estimate value-based weighted-average prices and potential cost savings under each policy.

Results: We identified 123 cancer drugs with 308 indications. Medicare and Medicaid spent a total of $28.2 billion on these drugs in 2020. Adopting value-based indication-specific pricing would increase drug prices by an average of 3.9%, with cost savings of $3.0 billion (10.6%). However, 43.7% higher prices for ultra-rare diseases would increase spending by 16.8% ($44 million). Adopting value-based weighted-average pricing would reduce prices by an average of 4.6% and spending by $3.0 billion (10.6%). Under weighted-average pricing, prices for and spending on ultra-rare diseases would be reduced by 22.6% and $55 million, respectively.

Conclusions: Value-based indication-specific and weighted-average pricing could help to align the value and price of new indications, thereby reducing expenditure on drugs with multiple indications.

Background: Although innovation generally provides measurable improvements in disease characteristics and patient survival, some benefits can remain unclear. This study aimed to investigate patient and healthcare provider (HCP) preferences for the innovative attributes of multiple myeloma (MM) treatments.

Methods: A cross-sectional, web-based, discrete choice experiment (DCE) survey was conducted among 200 patients with MM and 30 HCPs of patients with MM in the USA. A literature review, followed by interviews with patients with MM and HCPs, was undertaken to select five attributes (progression-free survival [PFS], chance of severe side effects, how patients live with MM treatments, scientific innovation, and monthly out-of-pocket [OOP] cost) and their levels. A Bayesian efficient design was used to generate DCE choice sets. Each choice set comprised two hypothetical MM treatment alternatives described by the selected attributes and their levels. Each patient and HCP was asked to choose a preferred alternative from each of the 11 choice sets. Mixed logit and latent class models were developed to estimate patient and HCP preferences for the treatment attributes.

Results: Overall, patients and HCPs preferred increased PFS, less chance of severe side effects, a treatment that offered life without treatment, scientific innovation, and lower OOP cost. From patients' perspectives, PFS had the highest conditional relative importance (44.7%), followed by how patients live with MM treatments (21.6%) and scientific innovation (16.0%).

Conclusions: In addition to PFS, patients and HCPs also valued innovative MM treatments that allowed them to live without treatments and/or offered scientific innovation. These attributes should be considered when evaluating MM treatments.

Background: Guidelines for modelling in economic evaluation recommend that it may be necessary to consider costs and outcomes until all modelled patients have died. Some guidelines also recommend that carers' health-related quality of life (HRQoL) outcomes should be included. However, it is unclear whether economic evaluations should continue to include carers' HRQoL after patients have died, and whether there is any evidence to support an additional bereavement effect for carers.

Methods: We used the UK Household Longitudinal Study waves 1-12. We used Difference-in-Differences to estimate the short- and long-term bereavement effects on the SF-6D for people who reported that they did and did not provide care to a household member who then died. We assumed parallel trends conditional on age, sex, long-term health conditions, education, and household income.

Results: Carers and non-carers experienced a significant loss in HRQoL in the year immediately following bereavement. Carers potentially experienced a loss in HRQoL in the year before bereavement, whereas the bereavement effect may have lasted longer for non-carers. For both groups, HRQoL became comparable to the non-bereaved population around 3 years after bereavement.

Conclusions: Bereavement has a statistically significant negative impact on HRQoL in the short-term, for both carers and non-carers. However, the effect size is small and is not sustained, suggesting that including bereavement in economic evaluation would make little difference to results.

Background: Capivasertib, a first-in-class AKT inhibitor, was recently approved as a second-line treatment for advanced breast cancer. However, capivasertib is expensive, raising questions over its economic value. This study provides the first evidence on the cost effectiveness of adding capivasertib to endocrine therapy (fulvestrant) for patients with PIK3CA/AKT1/PTEN-altered, hormone receptor-positive (HR⁺) human epidermal growth factor receptor 2-negative (HER2^-) advanced breast cancer.

Methods: A Markov model was built to compare the costs and effectiveness of three treatment strategies. The first strategy involved adding capivasertib to fulvestrant for all patients, while the second strategy involved adding it for only postmenopausal women. The third strategy involved treatment with fulvestrant alone. Analyses were conducted from a US payer perspective over a lifetime horizon. Costs were measured in 2023 US dollars, and effectiveness was measured in life years (LYs) and quality adjusted life years (QALYs), discounted at 3% per year. One-way sensitivity analyses, probabilistic sensitivity analyses, and scenario analyses were conducted to assess the robustness of results.

Results: The addition of capivasertib to fulvestrant for all patients was associated with $410,765 higher costs and 1.46 additional quality adjusted life years (QALYs) compared with fulvestrant alone, resulting in an incremental cost effectiveness ratio of $280,854/QALY. The strategy of adding capivasertib for only patients who are postmenopausal was extended dominated, i.e., yielded fewer QALYs at a higher cost per QALY than if capivasertib was added for all patients. These results were found to be robust in sensitivity and scenario analyses.

Conclusions: At its current price, our analysis suggests that the addition of capivasertib to fulvestrant as a second line treatment is not cost effective versus fulvestrant alone at a willingness-to-pay threshold of $100,000/QALY. The price of capivasertib will need to be reduced by nearly 70% (to $7000 per cycle) for it to become cost effective.