Assessing cost-utility of predictive biomarkers in oncology: a streamlined approach

Quantifying the Impact of Capacity Constraints in Economic Evaluations: An Application in Precision Medicine

BACKGROUND

Examples of precision medicine are complex interventions featuring both testing and treatment components. Because of this complexity, there are often barriers to the introduction of such interventions. Few economic evaluations attempt to determine the impact of these barriers on the cost-effectiveness of the intervention. This study presents a case study economic evaluation that illustrates how the value of implementation methods may be used to quantify the impact of capacity constraints in a decision-analytic model.

METHODS

A baseline decision-analytic model-based economic evaluation of ALK mutation testing was reproduced from a published technology appraisal. Three constraints (commissioning awareness, localization of testing, and pathology laboratory capacity) were identified using qualitative interviews, parameterized, and incorporated into the model. Value of implementation methods were used alongside incremental cost-effectiveness ratios (ICERs) to quantify the impact on the cost-effectiveness and net monetary benefit (NMB) of each capacity constraint and from the 3 constraints combined.

RESULTS

Each of the 3 capacity constraints resulted in a loss of NMB ranging from £7773 (0.1% of the total) per year for localized testing to £4,907,893 (77%) for a lack of awareness about commissioning ALK testing. When combined, the constraints resulted in a loss of NMB of £5,289,414 (83%). The localization and limited pathology capacity constraints slightly increased the ICER, but the lack of commissioning awareness constraint did not change the ICER.

CONCLUSIONS

Capacity constraints may have a significant impact on the NMB produced by examples of precision medicine. Value of implementation methods can be used to quantify the impact of such constraints by combining the impact of the constraints on the cost-effectiveness of the intervention with the impact on the number of patients receiving the intervention.

HIGHLIGHTS

While capacity constraints may prevent the use of precision medicine in clinical practice, economic evaluations rarely account for the impact of such barriers.This study demonstrates how constraints can be identified using qualitative methods and subsequently incorporated into decision-analytic models using quantitative value of implementation methods.In addition, this article demonstrates how value of implementation methods can be used to account for the impact of capacity constraints on the costs and benefits of an intervention as well as the number of patients receiving the intervention.In the case study presented herein, a capacity constraint reducing patient access to an example of precision medicine caused the biggest loss of net monetary benefit.Health economists should consider moving beyond incremental cost-effectiveness ratios to measures of total net monetary benefit to fully capture the impact of implementing precision medicine.

A State-of-the-Art Roadmap for Biomarker-Driven Drug Development in the Era of Personalized Therapies

Advances in biotechnology have enabled us to assay human tissue and cells to a depth and resolution that was never possible before, redefining what we know as the “biomarker”, and how we define a “disease”. This comes along with the shift of focus from a “one-drug-fits-all” to a “personalized approach”, placing the drug development industry in a highly dynamic landscape, having to navigate such disruptive trends. In response to this, innovative clinical trial designs have been key in realizing biomarker-driven drug development. Regulatory approvals of cancer genome sequencing panels and associated targeted therapies has brought personalized medicines to the clinic. Increasing availability of sophisticated biotechnologies such as next-generation sequencing (NGS) has also led to a massive outflux of real-world genomic data. This review summarizes the current state of biomarker-driven drug development and highlights examples showing the utility and importance of the application of real-world data in the process. We also propose that all stakeholders in drug development should (1) be conscious of and efficiently utilize real-world evidence and (2) re-vamp the way the industry approaches drug development in this era of personalized medicines.

Clinical and economic impact of 'ROS1-testing' strategy compared to a 'no-ROS1-testing' strategy in advanced NSCLC in Spain

Background

Detection of the ROS1 rearrangement is mandatory in patients with advanced or metastatic non-small cell lung cancer (NSCLC) to allow targeted therapy with specific inhibitors. However, in Spanish clinical practice ROS1 determination is not yet fully widespread. The aim of this study is to determine the clinical and economic impact of sequentially testing ROS1 in addition to EGFR and ALK in Spain.

Methods

A joint model (decision-tree and Markov model) was developed to determine the cost-effectiveness of testing ROS1 strategy versus a no-ROS1 testing strategy in Spain. Distribution of ROS1 techniques, rates of testing, positivity, and invalidity of biomarkers included in the analysis (EGFR, ALK, ROS1 and PD-L1) were based on expert opinion and Lungpath real-world database. Treatment allocation depending on the molecular testing results was defined by expert opinion. For each treatment, a 3-states Markov model was developed, where progression-free survival (PFS) and overall survival (OS) curves were parameterized using exponential extrapolations to model transition of patients among health states. Only medical direct costs were included (€ 2021). A lifetime horizon was considered and a discount rate of 3% was applied for both costs and effects. Both deterministic and probabilistic sensitivity analyses were performed to address uncertainty.

Results

A target population of 8755 patients with advanced NSCLC (non-squamous or never smokers squamous) entered the model. Over a lifetime horizon, the ROS1 testing scenario produced additional 157.5 life years and 121.3 quality-adjusted life years (QALYs) compared with no-ROS1 testing scenario. Total direct costs were increased up to € 2,244,737 for ROS1 testing scenario. The incremental cost-utility ratio (ICUR) was 18,514 €/QALY. Robustness of the base-case results were confirmed by the sensitivity analysis.

Conclusions

Our study shows that ROS1 testing in addition to EGFR and ALK is a cost-effective strategy compared to no-ROS1 testing, and it generates more than 120 QALYs in Spain over a lifetime horizon. Despite the low prevalence of ROS1 rearrangements in NSCLC patients, the clinical and economic consequences of ROS1 testing should encourage centers to test all advanced or metastatic NSCLC (non-squamous and never-smoker squamous) patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09397-4.