PharmacoEconomics最新文献

Background and objective: In cost-effectiveness analysis, treatment decisions are analysed at the population level. Combinations of treatment strategies that account for the heterogeneity of costs and effects across patients can be more cost-effective than a "one size fits all" approach. Individualized treatment rules (ITRs) assign a specific treatment to every patient based on their relevant characteristics, such that overall cost-effectiveness is optimized, but do not include feasibility or ethical considerations. We propose an approach for the design of ITRs based on simulated patient data from microsimulation models using statistical learning techniques.

Methods: We mathematically define the optimal ITR and how to measure the value of an ITR in a cost-effectiveness context. We explore least absolute shrinkage and selection operator (LASSO) regression, classification trees, and policy trees to illustrate how standard statistical learning techniques can be used to derive ITRs. We compare the strengths and limitations of these three approaches in terms of three criteria: the incremental value of the ITRs compared to optimal treatment assignment in terms of net monetary benefit (NMB), computational speed, and the interpretability of the ITRs. We propose methods to describe the impact of parameter uncertainty on the ITRs. We also explore how stochastic uncertainty can impact the ITR incremental value. We illustrate the methods by applying them to a microsimulation model for haemophilia B comparing four treatment strategies as a case study. The relevant patient characteristics in this model are the annualized bleeding rate, age, and sex.

Results: In our case study, a simple two-layer-deep classification tree is best suited based on the three criteria. This classification tree allocates treatments depending on whether the annualized bleeding rate of a patient is above or below 30 and whether their age is above or below 51. The optimal threshold values are uncertain based on the 95% credible ranges from the probabilistic analysis: 21-46 for annualized bleeding rate and 42-56 for age. Scenarios show that stochastic uncertainty has an impact on the incremental value of the ITR.

Discussion: Based on methodological considerations and the empirical findings in our case study, we expect the superiority of classification trees for the derivation of ITRs to be generalizable to other microsimulation models. This finding needs to be confirmed in future applications. Stochastic uncertainty has significant impacts on the ITRs, such that accurate representations of individual patient pathways are particularly crucial when designing ITRs. Future research could explore further empirical models and analytical approaches for ITRs or consider the translation of ITRs into the real-world decision-making context.

Cardiovascular disease (CVD) is a major contributor to the health and economic burden of disease globally. In this paper we discuss the literature on the health economics of the prevention and early intervention in CVD. We reveal the large economic impact of CVD and provide the economic argument supporting the calls for early detection and diagnosis of CVD outlined in the Global Heart Hub's patient-led Manifesto for Change. Many challenges in conducting cost-effectiveness analyses of interventions for CVD prevention are identified, as well as the emerging statistical and economic methods to help overcome these issues. Lastly, we acknowledge the profound disparities in cardiovascular health faced by minority or underserved populations, and the important role that prevention and early intervention can play in improving health equity.

Objective: We aimed to compare EQ-5D-Y-5L health state preferences among children, adolescents, and adults in Canada using a discrete choice experiment (DCE), and to explore the feasibility of a rescaling latent DCE using anchoring tasks collected from adolescents.

Methods: An online survey was conducted to elicit preferences for EQ-5D-Y-5L health states from children (aged 12-15 years), adolescents (aged 16-17 years), and adults (aged ≥ 18 years). All respondents completed 12 latent DCE tasks. Adults and adolescents were randomly assigned to three additional anchoring tasks using a DCE with duration or with dead. The tasks were framed from the perspective of a 10-year-old child for adults and their own perspective for children and adolescents. Respondents provided feedback on the difficulty of latent DCE tasks. Mixed logit models were used to analyze latent DCE data. Anchored DCE models using duration/dead tasks were estimated and compared between adults and adolescents.

Results: Overall, 546 children, 508 adolescents, and 908 adults were included in the analyses. A higher proportion of children indicated it easy to complete DCE tasks compared with adolescents and adults. Monotonicity of coefficients were observed in latent DCE models among adults but not among children and adolescents. Anchored DCE modeling performed better in adults than in adolescents regarding monotonicity and statistical significance of coefficients, and the DCE with duration performed slightly better than the DCE with dead.

Conclusions: There were differences in health state preferences elicited using DCEs between children/adolescents and adults. Anchoring tasks appeared feasible for adolescents, with a DCE with duration performing slightly better than a DCE with dead.

Background and objective: Health technology assessment (HTA) of haemato-oncology therapies typically requires extrapolation of long-term survival beyond a trial's follow-up. Health technology assessment agencies must balance caution around uncertainty in early follow-up trial data whilst aiming to provide timely access. This study qualitatively and quantitatively assessed how eight HTA agencies considered maturing data and external evidence.

Methods: The eight HTA appraisals were based on ZUMA-7, a phase III trial for axicabtagene ciloleucel (axi-cel) for second-line diffuse large B-cell lymphoma. ZUMA-7 survival data were submitted with either a 25-month ('Interim') or 47-month ('Primary') follow-up. To inform axi-cel Interim survival extrapolations, external evidence was available from a prior mature single-arm trial for third-line or later diffuse large B-cell lymphoma (ZUMA-1). A qualitative assessment of eight different submissions to HTA agencies was undertaken to determine key discussion points. The value and cost of waiting for evidence to mature between Interim and Primary analyses were quantified using value of information methods to evaluate the impact of waiting for further evidence collection on population health.

Results: Agencies used varied approaches to account for uncertainty in survival extrapolations in both Interim and Primary analyses. No agency considered external evidence fully during Interim submissions; one used it partially to inform clinical plausibility; four did not consider it. Health technology assessment agencies that did not consider the relevance of ZUMA-1 were more inclined to wait for more mature evidence to mitigate uncertainty. When ZUMA-1 aided in determining a plausible range for Interim extrapolations, the less valuable more mature evidence became, with the cost of waiting for Primary analysis results exceeding the value conferred.

Conclusions: There was limited consideration of external evidence during the included HTA submissions. In the future, it is recommended that external evidence should be considered to a greater degree by both manufacturers and HTA agencies when extrapolating survival to ensure appropriate and timely HTA decisions that minimise the undue burden on healthcare systems.

Background: Bayesian cost-effectiveness analysis (CEA) requires the specification of prior distributions for all parameters to be empirically estimated via Bayes' rule. When costs are modelled via Log-Normal distributions, Uniform prior distributions are commonly applied on the logarithm-scale standard deviations for costs due to the ease of implementation. However, the consequences of placing wide Uniform priors on standard deviations of log costs for the interpretation of original-scale CEA results remain unclear. The purpose of our study is to explore the impact of using Uniform priors for the standard deviations of cost data on CEA conclusions when costs are assumed to be log-normally distributed.

Methods: The analysis has been performed using individual-level cost-utility data from a randomised controlled trial. Costs are initially jointly modelled with quality-adjusted life years (QALYs) using Log-Normal and Beta distributions, respectively. Uniform prior distributions with different upper bounds are applied to log-scale standard deviations in the cost Log-Normal model. We compare the performance of Uniform priors under the Log-Normal distribution with other distributional assumptions for costs. A simulation study has then been conducted to explore the impact of these models and prior choices on cost estimates in CEAs.

Results: Results show that the choice of Uniform priors on standard deviations of log costs in a Log-Normal model can substantially induce large fluctuations in cost estimates, and thus potentially affect the final estimates of the intervention being cost-effective compared with other distributional assumptions. This is potentially driven by the occurrence of zero values in cost data.

Conclusion: Bayesian CEAs may be sensitive to the choice of upper bounds of the Uniform priors for the standard deviations of log costs in Log-Normal models, particularly when data contain zero values. Our results suggest that caution should be taken when Uniform distributions with large upper bounds are used.

Background and objectives: Metastatic castration-sensitive prostate cancer (mCSPC) imposes a significant economic burden and necessitates more cost-effective treatment strategies. The variability among the components of published economic evaluation models leads to methodological inconsistencies, underscoring the need for an optimal framework to minimise unwarranted structural variation. This paper reviews existing economic evaluations, establishes a comprehensive framework and aims to support future economic evaluations and decision-making in mCSPC.

Methods: A systematic literature review (SLR) was conducted to identify relevant economic evaluations in mCSPC. Health technology assessments (HTAs) by the National Institute for Health and Care Excellence, and Canada's Drug Agency were reviewed to gather insights on critiques and limitations. On the basis of these findings, a comprehensive cost-effectiveness modelling framework was established. Furthermore, two additional SLRs were conducted to identify cost and resource utilisation inputs, as well as health state utility scores derived from published studies and HTA assessments.

Results: Markov models and partitioned survival models (PSMs) were commonly reported in literature and published HTA evaluations. Despite the strong precedence of PSMs, we propose an optimal framework for mCSPC utilising a semi-Markov structure. This approach offers increased flexibility, allowing transition rates from progressed states to depend on time since progression occurred. We also present key sources of cost and utility data identified in the SLR.

Discussion: This work aligns with methodologies recommended by the Innovative Medicine Initiative (IMI) PIONEER external group and published studies. The optimal framework, including healthcare resource utilisation and utility data, consolidates existing modelling precedents in mCSPC and will assist the cost-effectiveness assessment of treatments for this condition.

Background and objective: Heart failure (HF) is a complex clinical syndrome associated with high mortality and extensive healthcare use. Using longitudinal data, we aimed to estimate the lifetime healthcare and long-term care (LTC) use and costs of Dutch patients with HF.

Methods: We used linked administrative data on mortality, LTC, and healthcare use covering the entire Dutch population for the period 2013-2024. Newly diagnosed patients with HF were defined as patients who were not hospitalized for HF 2 years before their index hospitalization for HF in 2015. Using regression modeling, we estimated hospitalized patients' life expectancy and lifetime healthcare costs as a function of age, sex, comorbidity, and income (all costs were adjusted to 2021 values).

Results: We identified 21,011 unique hospitalized patients with HF (mean age 80 years), of whom 86% died during follow-up. Estimated lifetime total healthcare and LTC costs varied between €35,000 and €170,000, depending on patient characteristics. Lifetime LTC costs varied between €9000 and €50,000. While comorbidity affects life expectancy substantially, it did not have a strong impact on lifetime costs. Income level affects costs more than comorbidities, and lower-income groups incur higher lifetime LTC costs.

Conclusions: Despite people with lower incomes having shorter lifespans than those with higher incomes, their lifetime LTC costs are higher. However, people with higher incomes have higher hospital costs, partly owing to their longer life expectancy.

Manufacturers of orphan drugs face several obstacles in meeting health technology assessment requirements because of poor availability of natural history data, small sample sizes, single-arm trials, and a paucity of established disease-specific endpoints. There is a need for specific considerations and modified approaches in health technology assessments that would account for the challenges in orphan drug development. Multistakeholder collaborations can benefit patients, their families, and the broader society and reduce the inequity faced by patients with rare diseases.

Introduction: When developing health economic simulation models, individual-level and cohort state-transition model types are commonly used. However, heterogeneity and the extent to which it is taken into account is thought to affect simulation outcomes differently in individual-level and cohort simulations, even when model structures are identical.

Objective: This study aimed to investigate the conditions under which the use of different model types may lead to different outcomes and therefore potentially different policy decisions.

Methods: A microsimulation model was used to reflect an individual-level simulation, simulating patient characteristics and, artificially, a cohort-level simulation of identical patients, using the exact same model structure. Four scenarios were analyzed: heterogeneity in age (scenario 1) influencing progression and recovery probabilities when on treatment, heterogeneity in sex (scenario 2) influencing progression and recovery probabilities when on treatment, combined heterogeneity in age and sex (scenario 3) influencing progression and recovery probabilities when on treatment, and heterogeneity in age when including age-dependent all-cause mortality (scenario 4). In every scenario, heterogeneity impact was varied, and health state occupancy, incremental costs, incremental effects, and the net monetary benefit of treatment versus no treatment were compared between the individual-level and cohort simulations.

Results: When introducing heterogeneity in age, sex, and age and sex combined, all scenarios showed differences between outcomes of individual-level and cohort simulations. However, these differences did not change the cost-effectiveness conclusions. When age influenced only age-dependent mortality, there were differences between the outcomes for the individual-level and cohort simulations when heterogeneity in age was introduced.

Conclusion: Patient heterogeneity can affect the outcomes of individual and cohort simulations differently, but reflecting more heterogeneity does not necessarily increase differences in simulation outcomes. However, age-dependent mortality affected analytic outcomes differently, suggesting a need for caution when developing cohort models if age is heterogeneous.