PharmacoEconomics最新文献

With an ever-increasing number of treatment options, the assessment of treatment sequences has become crucial in health technology assessment (HTA). This review systematically explores the multifaceted challenges inherent in evaluating sequences, delving into their interplay and nuances that go beyond economic model structures. We synthesised a 'roadmap' of literature from key methodological studies, highlighting the evolution of recent advances and emerging research themes. These insights were compared against HTA guidelines to identify potential avenues for future research. Our findings reveal a spectrum of challenges in sequence evaluation, encompassing selecting appropriate decision-analytic modelling approaches and comparators, deriving appropriate clinical effectiveness evidence in the face of data scarcity, scrutinising effectiveness assumptions and statistical adjustments, considering treatment displacement, and optimising model computations. Integrating methodologies from diverse disciplines-statistics, epidemiology, causal inference, operational research and computer science-has demonstrated promise in addressing these challenges. An updated review of application studies is warranted to provide detailed insights into the extent and manner in which these methodologies have been implemented. Data scarcity on the effectiveness of treatment sequences emerged as a dominant concern, especially because treatment sequences are rarely compared in clinical trials. Real-world data (RWD) provide an alternative means for capturing evidence on effectiveness and future research should prioritise harnessing causal inference methods, particularly Target Trial Emulation, to evaluate treatment sequence effectiveness using RWD. This approach is also adaptable for analysing trials harbouring sequencing information and adjusting indirect comparisons when collating evidence from heterogeneous sources. Such investigative efforts could lend support to reviews of HTA recommendations and contribute to synthesising external control arms involving treatment sequences.

Objective: This study aimed to assess the budget impact of introducing fixed-duration mosunetuzumab as a treatment option for adult patients with relapsed or refractory follicular lymphoma after at least two prior systemic therapies and to estimate the total cumulative costs per patient in the USA.

Methods: A 3-year budget impact model was developed for a hypothetical 1-million-member cohort enrolled in a mixed commercial/Medicare health plan. Comparators were: axicabtagene ciloleucel, tisagenlecleucel, tazemetostat, rituximab plus lenalidomide, copanlisib, and older therapies (rituximab or obinutuzumab ± chemotherapy). Costs per patient comprised treatment-associated costs including the drug, its administration, adverse events, and routine care. Dosing and safety data were ascertained from respective package inserts and clinical trial data. Drug costs (March 2023) were estimated based on the average wholesale acquisition cost reported in AnalySource^®, and all other costs were based on published sources and inflated to 2022 US dollars. Market shares were obtained from Genentech internal projections and expert opinion. Budget impact outcomes were presented on a per-member per-month basis.

Results: Compared with a scenario without mosunetuzumab, its introduction over 3 years resulted in a budget increase of $69,812 (1% increase) and an average per-member per-month budget impact of $0.0019. Among the newer therapies, mosunetuzumab had the second-lowest cumulative per patient cost (mosunetuzumab = $202,039; axicabtagene ciloleucel = $505,845; tisagenlecleucel = $476,293; rituximab plus lenalidomide = $263,520; tazemetostat = $250,665; copanlisib = $127,293) and drug costs, and its introduction only increased total drug costs by 0.1%. By year 3, the cumulative difference in the per patient cost with mosunetuzumab was -$303,805 versus axicabtagene ciloleucel, -$274,254 versus tisagenlecleucel, -$61,481 versus rituximab plus lenalidomide, -$48,625 versus tazemetostat, and $74,747 versus copanlisib. Older therapies were less costly with 3-year cumulative costs that ranged from $36,512 to $147,885.

Conclusions: Over 3 years, the estimated cumulative per patient cost of mosunetuzumab is lower than most available newer therapies, resulting in a small increase in the budget after its formulary adoption for the treatment of relapsed or refractory follicular lymphoma.

Background and Objective

Gene therapies for sickle cell disease (SCD) may offer meaningful benefits for patients and society. This study evaluated the cost-effectiveness of lovotibeglogene autotemcel (lovo-cel), a one-time gene therapy administered via autologous hematopoietic stem cell transplantation, compared with common care for patients in the United States (US) with SCD aged ≥ 12 years with ≥ 4 vaso-occlusive events (VOEs) in the past 24 months.

Methods

We developed a patient-level simulation model accounting for lovo-cel and SCD-related events, complications, and mortality over a lifetime time horizon. The pivotal phase 1/2 HGB-206 clinical trial (NCT02140554) served as the basis for lovo-cel efficacy and safety. Cost, quality-of-life, and other clinical data were sourced from HGB-206 data and the literature. Analyses were conducted from US societal and third-party payer perspectives. Uncertainty was assessed through probabilistic sensitivity analysis and extensive scenario analyses.

Results

Patients treated with lovo-cel were predicted to survive 23.84 years longer on average (standard deviation [SD], 12.80) versus common care (life expectancy, 62.24 versus 38.40 years), with associated discounted patient quality-adjusted life-year (QALY) gains of 10.20 (SD, 4.10) and direct costs avoided of $1,329,201 (SD, $1,346,446) per patient. Predicted societal benefits included discounted caregiver QALY losses avoided of 1.19 (SD, 1.38) and indirect costs avoided of $540,416 (SD, $262,353) per patient. Including lovo-cel costs ($3,282,009 [SD, $29,690] per patient) resulted in incremental cost-effectiveness ratios of $191,519 and $124,051 per QALY gained from third-party payer and societal perspectives, respectively. In scenario analyses, the predicted cost-effectiveness of lovo-cel also was sensitive to baseline age and VOE frequency and to the proportion of patients achieving and maintaining complete resolution of VOEs.

Conclusions

Our analysis of lovo-cel gene therapy compared with common care for patients in the US with SCD with recurrent VOEs estimated meaningful improvements in survival, quality of life, and other clinical outcomes accompanied by increased overall costs for the health care system and for broader society. The predicted economic value of lovo-cel gene therapy was influenced by uncertainty in long-term clinical effects and by positive spillover effects on patient productivity and caregiver burden.

Cost-effectiveness analyses commonly use population or sample averages, which can mask key differences across subgroups and may lead to suboptimal resource allocation. Despite there being several new methods developed over the last decade, there is no recent summary of what methods are available to researchers. This review sought to identify advances in methods for addressing patient heterogeneity in economic evaluations and to provide an overview of these methods. A literature search was conducted using the Econlit, Embase and MEDLINE databases to identify studies published after 2011 (date of a previous review on this topic). Eligible studies needed to have an explicit methodological focus, related to how patient heterogeneity can be accounted for within a full economic evaluation. Sixteen studies were included in the review. Methodologies were varied and included regression techniques, model design and value of information analysis. Recent publications have applied methodologies more commonly used in other fields, such as machine learning and causal forests. Commonly noted challenges associated with considering patient heterogeneity included data availability (e.g., sample size), statistical issues (e.g., risk of false positives) and practical factors (e.g., computation time). A range of methods are available to address patient heterogeneity in economic evaluation, with relevant methods differing according to research question, scope of the economic evaluation and data availability. Researchers need to be aware of the challenges associated with addressing patient heterogeneity (e.g., data availability) to ensure findings are meaningful and robust. Future research is needed to assess whether and how methods are being applied in practice.

Background and Objectives

There are significant challenges when obtaining clinical and economic evidence for health technology assessments of rare diseases. Many of them have been highlighted in previous systematic reviews but they have not been summarised in a comprehensive manner. For all stakeholders working with rare diseases, it is important to be aware and understand these issues. The objective of this review is to identify the main challenges for the economic evaluation of orphan drugs in rare diseases.

Methods

An umbrella review of systematic reviews of economic studies concerned with orphan and ultra-orphan drugs was conducted. Studies that were not systematic reviews, or on advanced therapeutic medicinal products, personalised medicines or other interventions that were not considered orphan drugs were excluded. The database searches included publications from 2010 to 2023, and were conducted in MEDLINE, EMBASE and the Cochrane library using filters for systematic reviews, and economic evaluations and models. These filters were combined with search terms for rare diseases and orphan drugs. A hand search supplemented the literature searches. The findings were reported by a compliant Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram.

Results

Two hundred and eighty-two records were identified from the literature searches, of which 64 were duplicates, whereas five reviews were identified from the hand search. A total of 36 reviews were included after screening against inclusion/exclusion criteria, 35 from literature searches and one from hand searching. Of those studies 1, 27 and 8 were low, moderate and high quality, respectively. The reviews highlight the scarcity of evidence for health economic parameters, for example, clinical effectiveness, costs, quality of life and the natural history of disease. Health economic evaluations such as cost-effectiveness and budget-impact analyses were scarce, and generally low-to-moderate quality. The causes were limited health economic parameters, together with publications bias, especially for cost-effectiveness analyses.

Conclusions

The results highlighted issues around a considerable paucity of evidence for economic evaluations and few cost-effectiveness analyses, supporting the notion that a paucity of evidence makes economic evaluations of rare diseases more challenging compared with more prevalent diseases. Furthermore, we provide recommendations for more sustainable approaches in economic evaluations of rare diseases.

Background

The current use of health economic decision models in HTA is mostly confined to single use cases, which may be inefficient and result in little consistency over different treatment comparisons, and consequently inconsistent health policy decisions, for the same disorder. Multi-use disease models (MUDMs) (other terms: generic models, whole disease models, disease models) may offer a solution. However, much is uncertain about their definition and application. The current research aimed to develop a blueprint for the application of MUDMs.

Methods

We elicited expert opinion using a two-round modified Delphi process. The panel consisted of experts and stakeholders in health economic modelling from various professional backgrounds. The first questionnaire concerned definition, terminology, potential applications, issues and recommendations for MUDMs and was based on an exploratory scoping review. In the second round, the panel members were asked to reconsider their input, based on feedback regarding first-round results, and to score issues and recommendations for priority. Finally, adding input from external advisors and policy makers in a structured way, an overview of issues and challenges was developed during two team consensus meetings.

Results

In total, 54 respondents contributed to the panel results. The term ‘multi-use disease models’ was proposed and agreed upon, and a definition was provided. The panel prioritized 10 potential applications (with comparing alternative policies and supporting resource allocation decisions as the top 2), while 20 issues (with model transparency and stakeholders’ roles as the top 2) were identified as challenges. Opinions on potential features concerning operationalization of multi-use models were given, with 11 of these subsequently receiving high priority scores (regular updates and revalidation after updates were the top 2).

Conclusions

MUDMs would improve on current decision support regarding cost-effectiveness information. Given feasibility challenges, this would be most relevant for diseases with multiple treatments, large burden of disease and requiring more complex models. The current overview offers policy makers a starting point to organize the development, use, and maintenance of MUDMs and to support choices concerning which diseases and policy decisions they will be helpful for.

Background and Objective

Decision models for health technology assessment (HTA) are largely submitted to HTA agencies using commercial software, which has known limitations. The use of the open-source programming language R has been suggested because of its efficiency, transparency, reproducibility, and ability to consider complex analyses. However, its use in HTA remains limited. This qualitative study aimed to explore the main reasons for this slow uptake of R in HTA and identify tangible facilitators.

Methods

We undertook two semi-structured focus group discussions with 24 key stakeholders from government agencies, consultancy, pharmaceutical companies, and academia. Two 1.5-hour discussions reflected on barriers identified in a previous study and highlighted additional barriers. Discussions were recorded and semi-transcribed, and data were organized and summarized into key themes.

Results

Human resources constraints were identified as a key barrier, including a lack of training, prioritization and collaboration, and resistance to change. Another key barrier was the lack of acceptance, or clear guidance, around submissions in R by HTA agencies. Participants also highlighted a lack of communication around accepted packages and decision model structures, and between HTA agencies on standard decision modeling structures.

Conclusions

There is a need for standardization, which can facilitate decision model sharing, coding homogeneity, and improved country adaptations. The creation of training materials and tailored workshops was identified as a key short-term facilitator. Increased communication and engagement of stakeholders could also facilitate the use of R by identifying needs and opportunities, encouraging HTA agencies to address structural barriers, and increasing incentives to use R.

Value of Information (VOI) analyses calculate the economic value that could be generated by obtaining further information to reduce uncertainty in a health economic decision model. VOI has been suggested as a tool for research prioritisation and trial design as it can highlight economically valuable avenues for future research. Recent methodological advances have made it increasingly feasible to use VOI in practice for research; however, there are critical differences between the VOI approach and the standard methods used to design research studies such as clinical trials. We aimed to highlight key differences between the research design approach based on VOI and standard clinical trial design methods, in particular the importance of considering the full decision context. We present two hypothetical examples to demonstrate that VOI methods are only accurate when (1) all feasible comparators are included in the decision model when designing research, and (2) all comparators are retained in the decision model once the data have been collected and a final treatment recommendation is made. Omitting comparators from either the design or analysis phase of research when using VOI methods can lead to incorrect trial designs and/or treatment recommendations. Overall, we conclude that incorrectly specifying the health economic model by ignoring potential comparators can lead to misleading VOI results and potentially waste scarce research resources.

Objectives

Accurate risk stratification of patients with stage II and III colorectal cancer (CRC) prior to treatment selection enables limited health resources to be efficiently allocated to patients who are likely to benefit from adjuvant chemotherapy. We aimed to investigate the cost-effectiveness of a recently developed deep learning-based prognostic method, Histotyping, from the perspective of the Norwegian healthcare system.

Methods

Two partitioned survival models were developed to assess the cost-effectiveness of Histotyping for two treatment cohorts: patients with CRC stage II and III. For each of the two cohorts, Histotyping was used for risk stratification to assign adjuvant chemotherapy and was compared with the standard of care (SOC) (adjuvant chemotherapy to all patients). Health outcomes measured in the model were quality-adjusted life years (QALYs) and life years (LYs) gained. Deterministic and probabilistic sensitivity analyses were performed to determine the impact of uncertainty. Scenario analyses were performed to assess the impact of the parameters with the greatest uncertainty.

Results

Risk-stratifying patients with CRC stage II and III using Histotyping was dominant (less costly and more effective) compared to SOC. In patients with CRC stage II, the net monetary benefit of Histotyping was 270,934 Norwegian kroners (NOK) (year of valuation is 2021), and the net health benefit of Histotyping was 0.99. In stage III, the net monetary benefit of Histotyping was 195,419 NOK, and the net health benefit of Histotyping was 0.71.

Conclusions

 Risk-stratifying patients with CRC using Histotyping prior to the administration of adjuvant chemotherapy is likely to be a cost-effective strategy in Norway.

Abstract