Surgical prioritization based on decision model outcomes is not sensitive to differences between the health-related quality of life values estimates of physicians and citizens.

PURPOSE: Decision models can be used to support allocation of scarce surgical resources. These models incorporate health-related quality of life (HRQoL) values that can be determined using physician panels. The predominant opinion is that one should use values obtained from citizens. We investigated whether physicians give different HRQoL values to citizens and evaluate whether such differences impact decision model outcomes. METHODS: A two-round Delphi study was conducted. Citizens estimated HRQoL of pre- and post-operative health states for ten surgeries using a visual analogue scale. These values were compared using Bland-Altman analysis with HRQoL values previously obtained from physicians. Impact on decision model outcomes was evaluated by calculating the correlation between the rankings of surgeries established using the physicians' and the citizens' values. RESULTS: A total of 71 citizens estimated HRQoL. Citizens' values on the VAS scale were - 0.07 points (95% CI - 0.12 to - 0.01) lower than the physicians' values. The correlation between the rankings of surgeries based on citizens' and physicians' values was 0.96 (p < 0.001). CONCLUSION: Physicians put higher values on health states than citizens. However, these differences only result in switches between adjacent entries in the ranking. It would seem that HRQoL values obtained from physicians are adequate to inform decision models during crises.

Second-Line Chimeric Antigen Receptor T-Cell Therapy in Diffuse Large B-Cell Lymphoma : A Cost-Effectiveness Analysis.

BACKGROUND: First-line treatment of diffuse large B-cell lymphoma (DLBCL) achieves durable remission in approximately 60% of patients. In relapsed or refractory disease, only about 20% achieve durable remission with salvage chemoimmunotherapy and consolidative autologous stem cell transplantation (ASCT). The ZUMA-7 (axicabtagene ciloleucel [axi-cel]) and TRANSFORM (lisocabtagene maraleucel [liso-cel]) trials demonstrated superior event-free survival (and, in ZUMA-7, overall survival) in primary-refractory or early-relapsed (high-risk) DLBCL with chimeric antigen receptor T-cell therapy (CAR-T) compared with salvage chemoimmunotherapy and consolidative ASCT; however, list prices for CAR-T exceed $400 000 per infusion. OBJECTIVE: To determine the cost-effectiveness of second-line CAR-T versus salvage chemoimmunotherapy and consolidative ASCT. DESIGN: State-transition microsimulation model. DATA SOURCES: ZUMA-7, TRANSFORM, other trials, and observational data. TARGET POPULATION: "High-risk" patients with DLBCL. TIME HORIZON: Lifetime. PERSPECTIVE: Health care sector. INTERVENTION: Axi-cel or liso-cel versus ASCT. OUTCOME MEASURES: Incremental cost-effectiveness ratio (ICER) and incremental net monetary benefit (iNMB) in 2022 U.S. dollars per quality-adjusted life-year (QALY) for a willingness-to-pay (WTP) threshold of $200 000 per QALY. RESULTS OF BASE-CASE ANALYSIS: The increase in median overall survival was 4 months for axi-cel and 1 month for liso-cel. For axi-cel, the ICER was $684 225 per QALY and the iNMB was -$107 642. For liso-cel, the ICER was $1 171 909 per QALY and the iNMB was -$102 477. RESULTS OF SENSITIVITY ANALYSIS: To be cost-effective with a WTP of $200 000, the cost of CAR-T would have to be reduced to $321 123 for axi-cel and $313 730 for liso-cel. Implementation in high-risk patients would increase U.S. health care spending by approximately $6.8 billion over a 5-year period. LIMITATION: Differences in preinfusion bridging therapies precluded cross-trial comparisons. CONCLUSION: Neither second-line axi-cel nor liso-cel was cost-effective at a WTP of $200 000 per QALY. Clinical outcomes improved incrementally, but costs of CAR-T must be lowered substantially to enable cost-effectiveness. PRIMARY FUNDING SOURCE: No research-specific funding.

Minimising population health loss in times of scarce surgical capacity: a modelling study for surgical procedures performed in nonacademic hospitals.

BACKGROUND: The burden of the COVID-19 pandemic resulted in a reduction of available health care capacity for regular care. To guide prioritisation of semielective surgery in times of scarcity, we previously developed a decision model to quantify the expected health loss due to delay of surgery, in an academic hospital setting. The aim of this study is to validate our decision model in a nonacademic setting and include additional elective surgical procedures. METHODS: In this study, we used the previously published three-state cohort state-transition model, to evaluate the health effects of surgery postponement for 28 surgical procedures commonly performed in nonacademic hospitals. Scientific literature and national registries yielded nearly all input parameters, except for the quality of life (QoL) estimates which were obtained from experts using the Delphi method. Two expert panels, one from a single nonacademic hospital and one from different nonacademic hospitals in the Netherlands, were invited to estimate QoL weights. We compared estimated model results (disability adjusted life years (DALY)/month of surgical delay) based on the QoL estimates from the two panels by calculating the mean difference and the correlation between the ranks of the different surgical procedures. The eventual model was based on the combined QoL estimates from both panels. RESULTS: Pacemaker implantation was associated with the most DALY/month of surgical delay (0.054 DALY/month, 95% CI: 0.025-0.103) and hemithyreoidectomy with the least DALY/month (0.006 DALY/month, 95% CI: 0.002-0.009). The overall mean difference of QoL estimates between the two panels was 0.005 (95% CI -0.014-0.004). The correlation between ranks was 0.983 (p < 0.001). CONCLUSIONS: Our study provides an overview of incurred health loss due to surgical delay for surgeries frequently performed in nonacademic hospitals. The quality of life estimates currently used in our model are robust and validate towards a different group of experts. These results enrich our earlier published results on academic surgeries and contribute to prioritising a more complete set of surgeries.

Emerging Therapies for COVID-19: The Value of Information From More Clinical Trials.

OBJECTIVES: The COVID-19 pandemic necessitates time-sensitive policy and implementation decisions regarding new therapies in the face of uncertainty. This study aimed to quantify consequences of approving therapies or pursuing further research: immediate approval, use only in research, approval with research (eg, emergency use authorization), or reject. METHODS: Using a cohort state-transition model for hospitalized patients with COVID-19, we estimated quality-adjusted life-years (QALYs) and costs associated with the following interventions: hydroxychloroquine, remdesivir, casirivimab-imdevimab, dexamethasone, baricitinib-remdesivir, tocilizumab, lopinavir-ritonavir, interferon beta-1a, and usual care. We used the model outcomes to conduct cost-effectiveness and value of information analyses from a US healthcare perspective and a lifetime horizon. RESULTS: Assuming a $100 000-per-QALY willingness-to-pay threshold, only remdesivir, casirivimab-imdevimab, dexamethasone, baricitinib-remdesivir, and tocilizumab were (cost-) effective (incremental net health benefit 0.252, 0.164, 0.545, 0.668, and 0.524 QALYs and incremental net monetary benefit $25 249, $16 375, $54 526, $66 826, and $52 378). Our value of information analyses suggest that most value can be obtained if these 5 therapies are approved for immediate use rather than requiring additional randomized controlled trials (RCTs) (net value $20.6 billion, $13.4 billion, $7.4 billion, $54.6 billion, and $7.1 billion), hydroxychloroquine (net value $198 million) is only used in further RCTs if seeking to demonstrate decremental cost-effectiveness and otherwise rejected, and interferon beta-1a and lopinavir-ritonavir are rejected (ie, neither approved nor additional RCTs). CONCLUSIONS: Estimating the real-time value of collecting additional evidence during the pandemic can inform policy makers and clinicians about the optimal moment to implement therapies and whether to perform further research.

Early Cost-Effectiveness of Onasemnogene Abeparvovec-xioi (Zolgensma) and Nusinersen (Spinraza) Treatment for Spinal Muscular Atrophy I in The Netherlands With Relapse Scenarios.

OBJECTIVES: Onasemnogene Abeparvovec-xioi (AVXS-101) is a gene therapy intended for curative treatment of spinal muscular atrophy (SMA) with an expected price of around €2 000 000. The goal of this study is to perform a cost-effectiveness analysis of treatment of SMA I patients with AVXS-101 in The Netherlands including relapse scenarios. METHODS: An individual-based state-transition model was used to model treatment effect and survival of SMA I patients treated with AVXS-101, nusinersen and best supportive care (BSC). The model included five health states: three health states according to SMA types, one for permanent ventilation and one for death. Deterministic and probabilistic sensitivity analyses were performed. Effects of relapsing to lower health states in the years following treatment was explored. RESULTS: The base-case incremental cost-effectiveness ratio (ICER) for AVXS-101 versus BSC is €138 875/QALY, and €53 447/QALY for AVXS-101 versus nusinersen. If patients relapse within 10 years after treatment with AVXS-101, the ICER can increase up to 6-fold, with effects diminishing thereafter. Only relapses occurring later than 50 years after treatment have a negligible effect on the ICER. To comply with Dutch willingness-to-pay reference values, the price of AVXS-101 must decrease to €680 000. CONCLUSIONS: Based on this model, treatment with AVXS-101 is unlikely to be cost-effective under Dutch willingness-to-pay reference values. Uncertainty regarding the long-term curative properties of AVXS-101 can result in multiplication of the ICER. Decision-makers are advised to appropriately balance these uncertainties against the price they are willing to pay now.

A Multidimensional Array Representation of State-Transition Model Dynamics.

Cost-effectiveness analyses often rely on cohort state-transition models (cSTMs). The cohort trace is the primary outcome of cSTMs, which captures the proportion of the cohort in each health state over time (state occupancy). However, the cohort trace is an aggregated measure that does not capture information about the specific transitions among health states (transition dynamics). In practice, these transition dynamics are crucial in many applications, such as incorporating transition rewards or computing various epidemiological outcomes that could be used for model calibration and validation (e.g., disease incidence and lifetime risk). In this article, we propose an alternative approach to compute and store cSTMs outcomes that capture both state occupancy and transition dynamics. This approach produces a multidimensional array from which both the state occupancy and the transition dynamics can be recovered. We highlight the advantages of the multidimensional array over the traditional cohort trace and provide potential applications of the proposed approach with an example coded in R to facilitate the implementation of our method.

A Need for Change! A Coding Framework for Improving Transparency in Decision Modeling.

The use of open-source programming languages, such as R, in health decision sciences is growing and has the potential to facilitate model transparency, reproducibility, and shareability. However, realizing this potential can be challenging. Models are complex and primarily built to answer a research question, with model sharing and transparency relegated to being secondary goals. Consequently, code is often neither well documented nor systematically organized in a comprehensible and shareable approach. Moreover, many decision modelers are not formally trained in computer programming and may lack good coding practices, further compounding the problem of model transparency. To address these challenges, we propose a high-level framework for model-based decision and cost-effectiveness analyses (CEA) in R. The proposed framework consists of a conceptual, modular structure and coding recommendations for the implementation of model-based decision analyses in R. This framework defines a set of common decision model elements divided into five components: (1) model inputs, (2) decision model implementation, (3) model calibration, (4) model validation, and (5) analysis. The first four components form the model development phase. The analysis component is the application of the fully developed decision model to answer the policy or the research question of interest, assess decision uncertainty, and/or to determine the value of future research through value of information (VOI) analysis. In this framework, we also make recommendations for good coding practices specific to decision modeling, such as file organization and variable naming conventions. We showcase the framework through a fully functional, testbed decision model, which is hosted on GitHub for free download and easy adaptation to other applications. The use of this framework in decision modeling will improve code readability and model sharing, paving the way to an ideal, open-source world.

Microsimulation Modeling for Health Decision Sciences Using R: A Tutorial.

Microsimulation models are becoming increasingly common in the field of decision modeling for health. Because microsimulation models are computationally more demanding than traditional Markov cohort models, the use of computer programming languages in their development has become more common. R is a programming language that has gained recognition within the field of decision modeling. It has the capacity to perform microsimulation models more efficiently than software commonly used for decision modeling, incorporate statistical analyses within decision models, and produce more transparent models and reproducible results. However, no clear guidance for the implementation of microsimulation models in R exists. In this tutorial, we provide a step-by-step guide to build microsimulation models in R and illustrate the use of this guide on a simple, but transferable, hypothetical decision problem. We guide the reader through the necessary steps and provide generic R code that is flexible and can be adapted for other models. We also show how this code can be extended to address more complex model structures and provide an efficient microsimulation approach that relies on vectorization solutions.