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.

Cost-effectiveness of postoperative imaging surveillance strategies for nonfunctional pituitary adenomas after resection with curative intent.

OBJECTIVE: The aim of this study was to determine an optimal follow-up imaging surveillance strategy in terms of cost-effectiveness after resection of nonfunctioning pituitary adenomas with curative intent. METHODS: An individual-level state-transition microsimulation model was used to simulate costs and outcomes associated with three postoperative imaging strategies over a lifetime time horizon: 1) annual MRI surveillance, 2) tapered MRI surveillance (annual surveillance for 5 years followed by surveillance every 2 years), and 3) personalized surveillance (annual surveillance for 5 years followed by surveillance every 2 years when MRI shows remnant disease/postoperative changes, and surveillance at 7, 10, and 15 years for disease-free MRI). Transition probabilities, utilities, and costs were estimated from recent published data and discounted by 3% annually. Model outcomes included lifetime costs (2022 US dollars), quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). RESULTS: Under base case assumptions, annual surveillance yielded higher costs and lower health effects (QALYs) compared with the tapered and personalized surveillance strategies (dominated). Personalized surveillance demonstrated an additional 0.1 QALY at additional cost ($1298) compared with tapered surveillance (7.7 QALYs at a cost of $12,862). The ICER was $11,793/QALY. The optimal decision was most sensitive to the probability of postoperative changes on MRI after surgery and MRI cost. Accounting for parameter uncertainty, personalized surveillance had a higher probability of being a cost-effective surveillance option compared with the alternative strategies at 79%. CONCLUSIONS: Using standard cost-effectiveness thresholds in the US ($100,000/QALY), personalized surveillance that accounted for remnant disease or postoperative changes on MRI was cost-effective compared with alternative surveillance strategies.

Minimizing population health loss due to scarcity in OR capacity: validation of quality of life input.

OBJECTIVES: A previously developed decision model to prioritize surgical procedures in times of scarce surgical capacity used quality of life (QoL) primarily derived from experts in one center. These estimates are key input of the model, and might be more context-dependent than the other input parameters (age, survival). The aim of this study was to validate our model by replicating these QoL estimates. METHODS: The original study estimated QoL of patients in need of commonly performed procedures in live expert-panel meetings. This study replicated this procedure using a web-based Delphi approach in a different hospital. The new QoL scores were compared with the original scores using mixed effects linear regression. The ranking of surgical procedures based on combined QoL values from the validation and original study was compared to the ranking based solely on the original QoL values. RESULTS: The overall mean difference in QoL estimates between the validation study and the original study was - 0.11 (95% CI: -0.12 - -0.10). The model output (DALY/month delay) based on QoL data from both studies was similar to the model output based on the original data only: The Spearman's correlation coefficient between the ranking of all procedures before and after including the new QoL estimates was 0.988. DISCUSSION: Even though the new QoL estimates were systematically lower than the values from the original study, the ranking for urgency based on health loss per unit of time delay of procedures was consistent. This underscores the robustness and generalizability of the decision model for prioritization of surgical procedures.

An Introductory Tutorial on Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.

Decision models can combine information from different sources to simulate the long-term consequences of alternative strategies in the presence of uncertainty. A cohort state-transition model (cSTM) is a decision model commonly used in medical decision making to simulate the transitions of a hypothetical cohort among various health states over time. This tutorial focuses on time-independent cSTM, in which transition probabilities among health states remain constant over time. We implement time-independent cSTM in R, an open-source mathematical and statistical programming language. We illustrate time-independent cSTMs using a previously published decision model, calculate costs and effectiveness outcomes, and conduct a cost-effectiveness analysis of multiple strategies, including a probabilistic sensitivity analysis. We provide open-source code in R to facilitate wider adoption. In a second, more advanced tutorial, we illustrate time-dependent cSTMs.

A Tutorial on Time-Dependent Cohort State-Transition Models in R Using a Cost-Effectiveness Analysis Example.

In an introductory tutorial, we illustrated building cohort state-transition models (cSTMs) in R, where the state transition probabilities were constant over time. However, in practice, many cSTMs require transitions, rewards, or both to vary over time (time dependent). This tutorial illustrates adding 2 types of time dependence using a previously published cost-effectiveness analysis of multiple strategies as an example. The first is simulation-time dependence, which allows for the transition probabilities to vary as a function of time as measured since the start of the simulation (e.g., varying probability of death as the cohort ages). The second is state-residence time dependence, allowing for history by tracking the time spent in any particular health state using tunnel states. We use these time-dependent cSTMs to conduct cost-effectiveness and probabilistic sensitivity analyses. We also obtain various epidemiological outcomes of interest from the outputs generated from the cSTM, such as survival probability and disease prevalence, often used for model calibration and validation. We present the mathematical notation first, followed by the R code to execute the calculations. The full R code is provided in a public code repository for broader implementation.

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.

Cost-effectiveness of direct surgery versus preoperative octreotide therapy for growth-hormone secreting pituitary adenomas.

PURPOSE: The objective of this study was to compare the cost-effectiveness of preoperative octreotide therapy followed by surgery versus the standard treatment modality for growth-hormone secreting pituitary adenomas, direct surgery (that is, surgery without preoperative treatment) from a public third-party payer perspective. METHODS: We developed an individual-level state-transition microsimulation model to simulate costs and outcomes associated with preoperative octreotide therapy followed by surgery and direct surgery for patients with growth-hormone secreting pituitary adenomas. Transition probabilities, utilities, and costs were estimated from recent published data and discounted by 3% annually over a lifetime time horizon. Model outcomes included lifetime costs [2020 United States (US) Dollars], quality-adjusted life-years (QALYs) and incremental cost-effectiveness ratios (ICERs). RESULTS: Under base case assumptions, direct surgery was found to be the dominant strategy as it yielded lower costs and greater health effects (QALYs) compared to preoperative octreotide strategy in the second-order Monte Carlo microsimulation. The ICER was most sensitive to probability of remission following primary therapy and duration of preoperative octreotide therapy. Accounting for joint parameter uncertainty, direct surgery had a higher probability of demonstrating a cost-effective profile compared to preoperative octreotide treatment at 77% compared to 23%, respectively. CONCLUSIONS: Using standard benchmarks for cost-effectiveness in the US ($100,000/QALY), preoperative octreotide therapy followed by surgery may not be cost-effective compared to direct surgery for patients with growth-hormone secreting pituitary adenomas but the result is highly sensitive to initial treatment failure and duration of preoperative treatment.

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.

Prioritisation and design of clinical trials.

Clinical trials require participation of numerous patients, enormous research resources and substantial public funding. Time-consuming trials lead to delayed implementation of beneficial interventions and to reduced benefit to patients. This manuscript discusses two methods for the allocation of research resources and reviews a framework for prioritisation and design of clinical trials. The traditional error-driven approach of clinical trial design controls for type I and II errors. However, controlling for those statistical errors has limited relevance to policy makers. Therefore, this error-driven approach can be inefficient, waste research resources and lead to research with limited impact on daily practice. The novel value-driven approach assesses the currently available evidence and focuses on designing clinical trials that directly inform policy and treatment decisions. Estimating the net value of collecting further information, prior to undertaking a trial, informs a decision maker whether a clinical or health policy decision can be made with current information or if collection of extra evidence is justified. Additionally, estimating the net value of new information guides study design, data collection choices, and sample size estimation. The value-driven approach ensures the efficient use of research resources, reduces unnecessary burden to trial participants, and accelerates implementation of beneficial healthcare interventions.

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.

Minimizing Population Health Loss in Times of Scarce Surgical Capacity During the Coronavirus Disease 2019 Crisis and Beyond: A Modeling Study.

OBJECTIVES: Coronavirus disease 2019 has put unprecedented pressure on healthcare systems worldwide, leading to a reduction of the available healthcare capacity. Our objective was to develop a decision model to estimate the impact of postponing semielective surgical procedures on health, to support prioritization of care from a utilitarian perspective. METHODS: A cohort state-transition model was developed and applied to 43 semielective nonpediatric surgical procedures commonly performed in academic hospitals. Scenarios of delaying surgery from 2 weeks were compared with delaying up to 1 year and no surgery at all. Model parameters were based on registries, scientific literature, and the World Health Organization Global Burden of Disease study. For each surgical procedure, the model estimated the average expected disability-adjusted life-years (DALYs) per month of delay. RESULTS: Given the best available evidence, the 2 surgical procedures associated with most DALYs owing to delay were bypass surgery for Fontaine III/IV peripheral arterial disease (0.23 DALY/month, 95% confidence interval [CI]: 0.13-0.36) and transaortic valve implantation (0.15 DALY/month, 95% CI: 0.09-0.24). The 2 surgical procedures with the least DALYs were placing a shunt for dialysis (0.01, 95% CI: 0.005-0.01) and thyroid carcinoma resection (0.01, 95% CI: 0.01-0.02). CONCLUSION: Expected health loss owing to surgical delay can be objectively calculated with our decision model based on best available evidence, which can guide prioritization of surgical procedures to minimize population health loss in times of scarcity. The model results should be placed in the context of different ethical perspectives and combined with capacity management tools to facilitate large-scale implementation.

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.

An Overview of R in Health Decision Sciences.

As the complexity of health decision science applications increases, high-level programming languages are increasingly adopted for statistical analyses and numerical computations. These programming languages facilitate sophisticated modeling, model documentation, and analysis reproducibility. Among the high-level programming languages, the statistical programming framework R is gaining increased recognition. R is freely available, cross-platform compatible, and open source. A large community of users who have generated an extensive collection of well-documented packages and functions supports it. These functions facilitate applications of health decision science methodology as well as the visualization and communication of results. Although R's popularity is increasing among health decision scientists, methodological extensions of R in the field of decision analysis remain isolated. The purpose of this article is to provide an overview of existing R functionality that is applicable to the various stages of decision analysis, including model design, input parameter estimation, and analysis of model outputs.