PharmacoEconomics最新文献

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: Testing high-risk populations for non-visible haematuria may enable earlier detection of bladder cancer, potentially decreasing mortality. This research aimed to assess the cost-effectiveness of urine dipstick screening for bladder cancer in high-risk populations in England.

Methods:  A microsimulation model developed in R software was calibrated to national incidence data by age, sex and stage, and validated against mortality data. Individual risk factors included age, sex, smoking status and factory employment. We evaluated three one-time screening scenarios: (1) current and former smokers of different ages within the 55-70 years range, (2) a mixed-age cohort of smokers aged 55-80 years and (3) individuals aged 65-79 years from high-risk regions. Probabilistic and scenario analyses evaluated uncertainty. The incremental cost-effectiveness ratio (ICER) was calculated and compared with the standard £20,000/quality-adjusted life year (QALY) threshold using payer's perspective and 2022 year of evaluation with 3.5% discounting for both costs and effects.

Results:  Screening all current and former smokers (scenario 1) and both mixed-age cohorts (scenarios 2 and 3) was not cost-effective at the threshold of £20,000/QALY. Screening at age 58 years had a 33% probability of being cost-effective at £20,000/QALY threshold and a 64% probability at £30,000/QALY threshold. Screening current and former smoking men aged 58 and 60 years was cost-effective, with ICERs of £18,181 and £18,425 per QALY, respectively. Scenario results demonstrated the high impact of assumptions on lead time, diagnostic pathway, and screening efficacy on predictions.

Conclusions:  Screening smoking men aged 58 or 60 years for bladder cancer using urine dipstick tests may be cost-effective.

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.

Background and objective: Approximately half of lung adenocarcinomas in East Asia harbor epidermal growth factor receptor (EGFR) mutations. EGFR testing followed by tissue-based next-generation sequencing (NGS), upfront tissue-based NGS, and complementary NGS approaches have emerged on the front line to guide personalized therapy. We study the cost effectiveness of exclusionary EGFR testing for Taiwanese patients newly diagnosed with advanced lung adenocarcinoma.

Methods: This economic evaluation was conducted from the perspective of the healthcare sector with a lifetime horizon. Simulated patients were entered into a joint model combining decision trees and partitioned survival models upon diagnosis of advanced lung adenocarcinoma. We compared exclusionary EGFR testing with upfront tissue-based NGS and complementary NGS approaches. The model inputs were derived from regional estimates (prevalence of targetable gene alterations), trials (testing accuracy, survival outcomes, and adverse events), ACT Genomics (testing costs), National Health Insurance payments, retail prices (drug costs), and hospital cohorts (utility values). All costs were made equivalent to 2023 US dollars. An annual discount rate of 3% was applied. We adopted a willingness-to-pay threshold of US$70,000 per quality-adjusted life-year. One-way deterministic and probabilistic analyses were performed.

Results: The incremental cost-effectiveness ratio of exclusionary EGFR testing versus upfront tissue-based NGS was US$15,521 per quality-adjusted life-year, whereas the incremental net monetary benefit was US$2530. The costs of osimertinib and pembrolizumab were the major determinants. The incremental net monetary benefit of exclusionary EGFR testing versus complementary NGS approach was US$2174, and its major determinants included the true-negative rate of EGFR testing and the prevalence rate of an EGFR mutation. Given the willingness-to-pay thresholds of US$35,000, US$70,000, and US$105,000 (1, 2, and 3 per capita gross domestic product) per quality-adjusted life-year, the probabilities that exclusionary EGFR testing would be cost effective were 79.1%, 95.6%, and 91.2%, respectively.

Conclusions: Our analysis suggests that exclusionary EGFR testing is a cost-effective strategy for Taiwanese patients newly diagnosed with advanced lung adenocarcinoma.

Background and objective: Hemophilia A is a costly, lifelong illness with multiple prophylaxis options. Previously, these options were assessed using a Peterson score-based model to simulate joint damage over time. This study built a model for the economic evaluation of hemophilia A with less socioeconomic selection bias utilizing the hemophilia joint health score (HJHS).

Methods: A mechanistically defined HJHS-based state-transition microsimulation model was implemented for the cost-utility analysis conducted over a lifetime horizon from a Canadian provincial Ministry of Health perspective, with a 1.5% discount rate on (costs and outcomes), to compare the following interventions: standard half-life (SHL), extended half-life (EHL), emicizumab, and efanesocotog alfa (EA). The health states are HJHS levels, waiting for surgery, postoperative time, and death. Individuals experience bleeds, joint bleeds (increasing the HJHS), and surgery in each health state. Disutilities include injections and postoperative time. Model validation included face validity, internal validity, comparison analysis, external validity, and predictive validity. Probabilistic analysis, pricing threshold analysis, and one-way scenario analyses were completed.

Results: EA showed lower levels of hospitalizations and surgeries and an improved joint damage experience in the simulation. However, EA was not cost-effective against emicizumab, which continued to be the most cost-effective intervention. Pricing threshold analysis indicated that a price decrease would be required for EA to dominate SHL (50% decrement) and emicizumab (55% decrement).

Conclusions: This is the first cost-effectiveness model incorporating HJHS to apply sequential joint damage to hemophilia A. While EA offers clinical benefits, our analysis suggests it will not be cost-effective from a Canadian provincial Ministry of Health perspective without a significant price decrease.

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.

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: Numerous health technology assessment guidance documents emphasize the importance of biological/clinical plausibility of modeled lifetime incremental survival without clearly defining it.

Objectives: This paper defines biologically and clinically plausible lifetime survival extrapolations and proposes a framework to systematically assess this by comparing survival expectations estimated premodeling, with the final modeled survival extrapolations. This framework is embedded in a survival extrapolation protocol template, which ensures that both the expectations and extrapolations are based on unified, comprehensive evidence.

Methods: A targeted review was conducted of 29 guidance documents from National Institute for Health and Care Excellence, Pharmaceutical Benefits Advisory Committee, Haute Autorité de Santé, Canada's Drug Agency, and European joint clinical assessment, focusing on survival analysis, evidence synthesis, cost-effectiveness modeling methods, and use of observational data.

Results: Survival extrapolations are biologically/clinically plausible when "predicted survival estimates that fall within the range considered plausible a-priori, obtained using a-priori justified methodology." These a priori expectations should utilize the totality of evidence available and take into account local target setting (i.e., survival-influencing aspects such as patient population, treatment pathway, and country). Pre-protocolized biologically/clinically plausible survival extrapolation was operationalized in a five-step DICSA approach: (1) Describe the target setting as defined by all relevant treatment and disease aspects that influence survival; (2) collect Information from relevant sources; (3) Compare survival-influencing aspects across information sources; (4) Set pre-protocolized survival expectations and plausible ranges; and (5) Assess how trial-based extrapolations align with the set expectations by comparing modeled survival extrapolations to the range of values a priori considered to be plausible.

Conclusion: The definition of plausibility of survival extrapolations, the operationalization of its assessment, and the corresponding extrapolation protocol template can contribute to the transparent development of biologically/clinically plausible survival extrapolations.