Consolidated Health Economic Evaluation Reporting Standards - Value of Information (CHEERS-VOI): Explanation and Elaboration

Making Drug Approval Decisions in the Face of Uncertainty: Cumulative Evidence versus Value of Information

BACKGROUND

The COVID-19 pandemic underscored the criticality and complexity of decision making for novel treatment approval and further research. Our study aims to assess potential decision-making methodologies, an evaluation vital for refining future public health crisis responses.

METHODS

We compared 4 decision-making approaches to drug approval and research: the Food and Drug Administration's policy decisions, cumulative meta-analysis, a prospective value-of-information (VOI) approach (using information available at the time of decision), and a reference standard (retrospective VOI analysis using information available in hindsight). Possible decisions were to reject, accept, provide emergency use authorization, or allow access to new therapies only in research settings. We used monoclonal antibodies provided to hospitalized COVID-19 patients as a case study, examining the evidence from September 2020 to December 2021 and focusing on each method's capacity to optimize health outcomes and resource allocation.

RESULTS

Our findings indicate a notable discrepancy between policy decisions and the reference standard retrospective VOI approach with expected losses up to $269 billion USD, suggesting suboptimal resource use during the wait for emergency use authorization. Relying solely on cumulative meta-analysis for decision making results in the largest expected loss, while the policy approach showed a loss up to $16 billion and the prospective VOI approach presented the least loss (up to $2 billion).

CONCLUSION

Our research suggests that incorporating VOI analysis may be particularly useful for research prioritization and treatment implementation decisions during pandemics. While the prospective VOI approach was favored in this case study, further studies should validate the ideal decision-making method across various contexts. This study's findings not only enhance our understanding of decision-making strategies during a health crisis but also provide a potential framework for future pandemic responses.

HIGHLIGHTS

This study reviews discrepancies between a reference standard (retrospective VOI, using hindsight information) and 3 conceivable real-time approaches to research-treatment decisions during a pandemic, suggesting suboptimal use of resources.Of all prospective decision-making approaches considered, VOI closely mirrored the reference standard, yielding the least expected value loss across our study timeline.This study illustrates the possible benefit of VOI results and the need for evidence accumulation accompanied by modeling in health technology assessment for emerging therapies.

First-line treatments for advanced non-squamous non-small cell lung cancer with immune checkpoint inhibitors plus chemotherapy: a systematic review, network meta-analysis, and cost-effectiveness analysis

Introduction

The combination of immune checkpoint inhibitors (ICIs) and chemotherapy is a promising first-line therapy for patients with advanced non-squamous non-small cell lung cancer (NSCLC). The cost-effectiveness of combinations with different ICIs is yet to be compared.

Methods

We utilized Bayesian network meta-analyses for the comparisons of overall survival, progression-free survival, and incidence of adverse events of the included treatments in the total population and subgroups with different programmed death-ligand 1 tumor proportional scores (TPS). The cost-effectiveness of the treatments from the perspectives of the US and Chinese healthcare systems was assessed using Markov models.

Results

Three combinations, including pembrolizumab + chemotherapy (PembroC), nivolumab + ipilimumab + chemotherapy (NivoIpiC), and atezolizumab + chemotherapy (AteC), were included in our study. In terms of efficacy, PembroC was most likely to be ranked first for extending progression-free survival (PFS) (93.16%) and overall survival (OS) (90.73%). Nevertheless, from the US perspective, NivoIpiC and PembroC showed incremental cost-effectiveness ratios (ICERs) of $68,963.1/quality-adjusted life-years (QALY) and $179,355.6/QALY, respectively, compared with AteC. The one-way sensitivity analysis revealed that the results were primarily sensitive to the hazard ratios for OS or the cost of immunotherapy agents. At a willingness-to-pay (WTP) threshold of $150,000/QALY, NivoIpiC had the highest probability of being cost-effective (63%). As for the Chinese perspective, NivoIpiC and PembroC had ICERs of $145,983.4/QALY and $195,863.3/QALY versus AteC, respectively. The results were primarily sensitive to the HRs for OS. At a WTP threshold of $38,017/QALY, AteC had the highest probability of cost-effectiveness (94%).

Conclusion

Although PembroC has the optimal efficacy, NivoIpiC and AteC were the most favorable treatments in terms of cost-effectiveness for patients with advanced non-squamous NSCLC from the US and Chinese perspectives, respectively.

Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI)

OBJECTIVES

Economic evaluations (EEs) are commonly used by decision makers to understand the value of health interventions. The Consolidated Health Economic Evaluation Reporting Standards (CHEERS 2022) provide reporting guidelines for EEs. Healthcare systems will increasingly see new interventions that use artificial intelligence (AI) to perform their function. We developed Consolidated Health Economic Evaluation Reporting Standards for Interventions that use AI (CHEERS-AI) to ensure EEs of AI-based health interventions are reported in a transparent and reproducible manner.

METHODS

Potential CHEERS-AI reporting items were informed by 2 published systematic literature reviews of EEs and a contemporary update. A Delphi study was conducted using 3 survey rounds to elicit multidisciplinary expert views on 26 potential items, through a 9-point Likert rating scale and qualitative comments. An online consensus meeting was held to finalize outstanding reporting items. A digital health patient group reviewed the final checklist from a patient perspective.

RESULTS

A total of 58 participants responded to survey round 1, 42, and 31 of whom responded to rounds 2 and 3, respectively. Nine participants joined the consensus meeting. Ultimately, 38 reporting items were included in CHEERS-AI. They comprised the 28 original CHEERS 2022 items, plus 10 new AI-specific reporting items. Additionally, 8 of the original CHEERS 2022 items were elaborated on to ensure AI-specific nuance is reported.

CONCLUSIONS

CHEERS-AI should be used when reporting an EE of an intervention that uses AI to perform its function. CHEERS-AI will help decision makers and reviewers to understand important AI-specific details of an intervention, and any implications for the EE methods used and cost-effectiveness conclusions.

A Guide to an Iterative Approach to Model-Based Decision Making in Health and Medicine: An Iterative Decision-Making Framework

Decision makers frequently face decisions about optimal resource allocation. A model-based economic evaluation can be used to guide decision makers in their choices by systematically evaluating the magnitude of expected health effects and costs of decision options and by making trade-offs explicit. We provide a guide to an iterative approach to the medical decision-making process by following a coherent framework, and outline the overarching iterative steps of model-based decision making. We systematized the framework by performing three steps. First, we compiled the existing guidelines provided by the ISPOR-SMDM Modeling Good Research Practices Task Force, and the ISPOR Value of Information Task Force. Second, we identified other previous work related to frameworks and guidelines for model-based decision analyses through a literature search in PubMed. Third, we assessed the role of the evidence and iterative process in decision making and formalized key steps in a model-based decision-making framework. We provide guidance on an iterative approach to medical decision making by applying the compiled iterative model-based decision-making framework. The framework formally combines the decision problem conceptualization (Part I), the model conceptualization and development (Part II), and the process of model-based decision analysis (Part III). Following the overarching steps of the framework ensures compliance to the principles of evidence-based medicine and regular updates of the evidence, given that value of information analysis represents an essential component of model-based decision analysis in the framework. Following the provided guide and the steps outlined in the framework can help inform various health care decisions, and therefore it has the potential to improve decision making.

Early Cost-Effectiveness Analysis of Cytoreductive Surgery and Hyperthermic Intraperitoneal Chemotherapy for Gastric Cancer Patients with Limited Peritoneal Carcinomatosis

BACKGROUND

Gastric cancer patients with peritoneal carcinomatosis (PC) have a poor prognosis, with a median overall survival of 10 months when treated with systemic chemotherapy only. Cohort studies showed that cytoreductive surgery with hyperthermic intraperitoneal chemotherapy (CRS/HIPEC) might improve the prognosis for gastric cancer patients with limited PC. Besides generating trial data on clinical effectiveness, it is crucial to timely collect information on economic aspects to guide the reimbursement decision-making process. No previous data have been published on the cost(-effectiveness) of CRS/HIPEC in this group of patients. Therefore, we performed an early model-based cost-effectiveness analysis of CRS/HIPEC for gastric cancer patients with limited PC in the Dutch setting.

METHODS

We constructed a two-state (alive-dead) Markov transition model to evaluate costs and clinical outcomes from a Dutch healthcare perspective. Clinical outcomes, transition probabilities and utilities were derived from literature and verified by clinical experts in the field. Costs were measured using two available representative cohorts (2010-2017): one 'systemic chemotherapy only' cohort and one 'CRS/HIPEC' cohort (n = 10 each). Incremental cost-utility ratios (ICURs) were expressed as Euros per quality-adjusted life-year (QALY). We performed probabilistic and deterministic sensitivity, scenario, and value-of-information analyses using a willingness-to-pay (WTP) threshold of €80,000/QALY, which reflects the Dutch norm for severe diseases.

RESULTS

In the base-case analysis, CRS/HIPEC yielded more QALYs (increment of 0.68) and more costs (increment of €34,706) compared with systemic chemotherapy only, resulting in an ICUR of €50,990/QALY. The probability that CRS/HIPEC was cost effective compared with systemic chemotherapy alone was 64%. To reduce uncertainty, the expected value of perfect information amounted to €4,021,468. The scenario analyses did not alter the results and showed that treatment costs, lifetime health-related quality of life and overall survival had the largest influence on the model.

CONCLUSIONS

The presented early cost-effectiveness analysis suggests that adding CRS/HIPEC to systemic chemotherapy for gastric cancer patients with limited PC has a good chance of being cost-effectiveness compared with systemic chemotherapy alone when using a WTP of €80,000/QALY. However, there is substantial uncertainty in view of the current available data on effectiveness. Results from the ongoing phase III PERISCOPE II trial are therefore crucial for further decisions on treatment policy and its cost-effectiveness.