Critical Examination of Modeling Approaches Used in Economic Evaluations of First-Line Treatments for Locally Advanced or Metastatic Non-Small Cell Lung Cancer Harboring Epidermal Growth Factor Receptor Mutations: A Systematic Literature Review.

BACKGROUND: Non-small cell lung cancer (NSCLC) is the most common type of lung cancer, with up to 32% of patients with NSCLC harboring an epidermal growth factor receptor (EGFR) mutation. NSCLC harboring an EGFR mutation has a dedicated treatment pathway, with EGFR tyrosine kinase inhibitors and platinum-based chemotherapy often being the therapy of choice. OBJECTIVE: The aim of this study was to systemically review and summarize economic models of first-line treatments used for locally advanced or metastatic NSCLC harboring EGFR mutations, as well as to identify areas for improvement for future models. METHODS: Literature searches were conducted via Ovid in PubMed, MEDLINE, MEDLINE In-Process, Embase, Evidence-Based Medicine Reviews: Health Technology Assessment, Evidence-Based Medicine Reviews: National Health Service Economic Evaluation Database, and EconLit. An initial search was conducted on 19 December 2022 and updated on 11 April 2023. Studies were selected according to predefined criteria using the Population, Intervention, Comparator, Outcome and Study design (PICOS) framework. RESULTS: Sixty-seven articles were included in the review, representing 59 unique studies. The majority of included models were cost-utility analyses (n = 52), with the remaining studies being cost-effectiveness analyses (n = 4) and a cost-minimization analysis (n = 1). Two studies incorporated both a cost-utility and cost-minimization analysis. Although the model structure across studies was consistently reported, justification for this choice was often lacking. CONCLUSIONS: Although the reporting of economic models in NSCLC harboring EGFR mutations is generally good, many of these studies lacked sufficient reporting of justification for structural choices, performing extensive sensitivity analyses and validation in economic evaluations. In resolving such gaps, the validity of future models can be increased to guide healthcare decision making in rare indications.

Cost-effectiveness of direct-acting antivirals for chronic hepatitis C virus in the United States from a payer perspective.

BACKGROUND: Direct-acting antivirals (DAAs) have been a breakthrough therapeutic innovation in the treatment of chronic hepatitis C virus (HCV) with significantly improved efficacy, safety, and tolerability. OBJECTIVE: To evaluate the cost-effectiveness of treating patients with HCV with DAAs compared with pre-DAAs or no treatment over a lifetime horizon from the perspective of the US Veterans Affairs (VA) health care system. METHODS: A hybrid decision-tree and Markov model simulated the health outcomes of a cohort of 142,147 patients with HCV with an average age of 63 years. Demographic data, treatment rates and distribution, treatment efficacy by subpopulation, and health state costs were sourced from VA data. Treatment costs and utility values were sourced from publicly available databases and prior publications for older regimens. RESULTS: Over a lifetime horizon, the use of DAAs results in a significant reduction in advanced liver disease events compared with pre-DAA and no treatment. Total cost savings of $7 and $9 billion over a lifetime horizon (50 years) were predicted for patients who received DAA treatments compared with patients treated with pre-DAA treatments and those who were untreated, respectively. Cost savings were achieved quickly after treatment, with DAAs being inexpensive when compared with both the pre-DAA and untreated scenarios within 5 years. The DAA intervention dominated (ie, more effective and less costly) for both the pre-DAA and untreated strategies on both a per-patient and cohort basis. CONCLUSIONS: The use of DAA-based treatments in patients with HCV in the VA system significantly reduced long-term HCV-related morbidity and mortality, while providing cost savings within only 5 years of treatment. DISCLOSURES: This work was supported by Gilead Inc. Health Economic Outcomes Research group, grant number GS-US-18-HCV003. Drs Yehoshua and Kaushik are employees of Gilead in the Health Economic Outcome Research group. These individuals reviewed the manuscript but did not contribute to input or output of the Markov model. Maple Health Group (Dr El-Moustaid, Ms Raad, and Dr Smith) are consultants hired by Gilead for Markov modeling expertise. The model used in this study was previously published and peer reviewed. Data inputted into the model related to patient demographic, treatment outcomes, clinical outcomes, and costs were completely independent in derivation by Drs Kaplan, Serper, and Durkin and were not influenced by the funding sponsor. Dr Kaplan reports grants from Gilead Inc. during the conduct of the study and grants from Gilead Inc., other from Glycotest Inc., other from AstraZeneca, other from Exact Sciences, and other from Bayer outside the submitted work.

Integrated vaccination and non-pharmaceutical interventions based strategies in Ontario, Canada, as a case study: a mathematical modelling study.

Recently, two coronavirus disease 2019 (COVID-19) vaccine products have been authorized in Canada. It is of crucial importance to model an integrated/combined package of non-pharmaceutical (physical/social distancing) and pharmaceutical (immunization) public health control measures. A modified epidemiological, compartmental SIR model was used and fit to the cumulative COVID-19 case data for the province of Ontario, Canada, from 8 September 2020 to 8 December 2020. Different vaccine roll-out strategies were simulated until 75% of the population was vaccinated, including a no-vaccination scenario. We compete these vaccination strategies with relaxation of non-pharmaceutical interventions. Non-pharmaceutical interventions were supposed to remain enforced and began to be relaxed on 31 January, 31 March or 1 May 2021. Based on projections from the data and long-term extrapolation of scenarios, relaxing the public health measures implemented by re-opening too early would cause any benefits of vaccination to be lost by increasing case numbers, increasing the effective reproduction number above 1 and thus increasing the risk of localized outbreaks. If relaxation is, instead, delayed and 75% of the Ontarian population gets vaccinated by the end of the year, re-opening can occur with very little risk. Relaxing non-pharmaceutical interventions by re-opening and vaccine deployment is a careful balancing act. Our combination of model projections from data and simulation of different strategies and scenarios, can equip local public health decision- and policy-makers with projections concerning the COVID-19 epidemiological trend, helping them in the decision-making process.

Could a New COVID-19 Mutant Strain Undermine Vaccination Efforts? A Mathematical Modelling Approach for Estimating the Spread of B.1.1.7 Using Ontario, Canada, as a Case Study.

Infections represent highly dynamic processes, characterized by evolutionary changes and events that involve both the pathogen and the host. Among infectious agents, viruses, such as Severe Acute Respiratory Syndrome-related Coronavirus type 2 (SARS-CoV-2), the infectious agent responsible for the currently ongoing Coronavirus disease 2019 (COVID-2019) pandemic, have a particularly high mutation rate. Taking into account the mutational landscape of an infectious agent, it is important to shed light on its evolution capability over time. As new, more infectious strains of COVID-19 emerge around the world, it is imperative to estimate when these new strains may overtake the wild-type strain in different populations. Therefore, we developed a general-purpose framework to estimate the time at which a mutant variant is able to take over a wild-type strain during an emerging infectious disease outbreak. In this study, we used COVID-19 as a case-study; however, the model is adaptable to any emerging pathogen. We devised a two-strain mathematical framework to model a wild- and a mutant-type viral population and fit cumulative case data to parameterize the model, using Ontario as a case study. We found that, in the context of under-reporting and the current case levels, a variant strain was unlikely to dominate until March/April 2021. The current non-pharmaceutical interventions in Ontario need to be kept in place longer even with vaccination in order to prevent another outbreak. The spread of a variant strain in Ontario will likely be observed by a widened peak of the daily reported cases. If vaccine efficacy is maintained across strains, then it is still possible to achieve high levels of immunity in the population by the end of 2021. Our findings have important practical implications in terms of public health as policy- and decision-makers are equipped with a mathematical tool that can enable the estimation of the take-over of a mutant strain of an emerging infectious disease.

Cost-Effectiveness of Durvalumab After Chemoradiotherapy in Unresectable Stage III NSCLC: A US Healthcare Perspective.

BACKGROUND: Durvalumab was approved by the FDA in February 2018 for patients with unresectable stage III NSCLC that has not progressed after platinum-based concurrent chemoradiotherapy (cCRT), and this regimen is the current standard of care. The objective of this study was to examine the cost-effectiveness of durvalumab following cCRT versus cCRT alone in patients with locally advanced, unresectable stage III NSCLC. METHODS: A 3-state semi-Markov model was used. Modeling was performed in a US healthcare setting from Medicare and commercial payer perspectives over a 30-year time horizon. Clinical efficacy (progression-free and post progression survival) and utility inputs were based on PACIFIC study data (ClinicalTrials.gov identifier: NCT02125461; data cutoff March 22, 2018). Overall survival extrapolation was validated using overall survival data from a later data cutoff (January 31, 2019). The main outcome was the incremental cost-effectiveness ratio (ICER) of durvalumab following cCRT versus cCRT alone, calculated as the difference in total costs between treatment strategies per quality-adjusted life-year (QALY) gained. RESULTS: In the base-case analysis, durvalumab following cCRT was cost-effective versus cCRT alone from Medicare and commercial insurance perspectives, with ICERs of $55,285 and $61,111, respectively, per QALY gained. Durvalumab was thus considered cost-effective at the $100,000 willingness-to-pay (WTP) threshold. Sensitivity analyses revealed the model was particularly affected by variables associated with subsequent treatment, although no tested variable increased the ICER above the WTP threshold. Scenario analyses showed the model was most sensitive to assumptions regarding time horizon, treatment effect duration, choice of fitted progression-free survival curve, subsequent immunotherapy treatment duration, and use of a partitioned survival model structure. CONCLUSIONS: In a US healthcare setting, durvalumab was cost-effective compared with cCRT alone, further supporting the adoption of durvalumab following cCRT as the new standard of care in patients with unresectable stage III NSCLC.