What is the Impact of the Analysis Method Used for Health State Utility Values on QALYs in Oncology? A Simulation Study Comparing Progression-Based and Time-to-Death Approaches

Background: Health state utility values (‘utilities’) are an integral part of health technology assessment. Though traditionally categorised by disease status in oncology (i.e. progression), several recent assessments have adopted values calculated according to the time that measures were recorded before death. We conducted a simulation study to understand the limitations of each approach, with a focus on mismatches between the way utilities are generated, and analysed. Methods: Survival times were simulated based on published literature, with permutations of three utility generation mechanisms (UGMs) and utility analysis methods (UAMs): (1) progression based, (2) time-to-death based, and (3) a ‘combination approach’. For each analysis quality-adjusted life-years (QALYs) were estimated. Goodness of fit was assessed via percentage mean error (%ME) and mean absolute error (%MAE). Scenario analyses were performed varying individual parameters, with complex scenarios mimicking published studies. The statistical code is provided for transparency and to aid future work in the area. Results: %ME and %MAE were lowest when the correct analysis form was specified (i.e. UGM and UAM aligned). Underestimates were produced when a time-to-death element was present in the UGM but not included in the UAM, while the ‘combined’ UAM produced overestimates irrespective of the UGM. Scenario analysis demonstrated the importance of the volume of available data beyond the initial time period, for example follow-up. Conclusions: We show that the use of an incorrectly or over-specified UAM can result in substantial bias in the estimation of utilities. We present a flowchart to highlight the issues that may be faced

MPES-R:Multi-Parameter Evidence Synthesis in R for survival extrapolation – A tutorial

Survival extrapolation often plays an important role in health technology assessment (HTA), and there are a range of different approaches available. Approaches that can leverage external evidence (i.e., data or information collected outside the main data source of interest) may be helpful, given the extent of uncertainty often present when determining a suitable survival extrapolation. One of these methods is the multi-parameter evidence synthesis (MPES) approach, first proposed for use in HTA by Guyot et al, and more recently by Jackson. While MPES has potential benefits over conventional extrapolation approaches (such as simple or flexible parametric models), it is more computationally complex and requires use of specialist software. This tutorial presents an introduction to MPES for HTA, alongside a user friendly, publicly available operationalisation of Guyot’s original MPES that can be executed using the statistical software package R. Through two case studies, both Guyot’s and Jackson’s MPES approaches are explored, along with sensitivity analyses relevant to HTA. Finally, the discussion section of the tutorial details important considerations for analysts considering use of an MPES approach, along with potential further developments. MPES has not been used often in HTA, and so there are limited examples of how it has been used and perceived. However, this tutorial may aid future research efforts exploring the use of MPES further

Probabilistic Sensitivity Analysis in Cost-Effectiveness Models: Determining Model Convergence in Cohort Models.

Probabilistic sensitivity analysis (PSA) demonstrates the parameter uncertainty in a decision problem. The technique involves sampling parameters from their respective distributions (rather than simply using mean/median parameter values). Guidance in the literature, and from health technology assessment bodies, on the number of simulations that should be performed suggests a 'sufficient number', or until 'convergence', which is seldom defined. The objective of this tutorial is to describe possible outcomes from PSA, discuss appropriate levels of accuracy, and present guidance by which an analyst can determine if a sufficient number of simulations have been conducted, such that results are considered to have converged. The proposed approach considers the variance of the outcomes of interest in cost-effectiveness analysis as a function of the number of simulations. A worked example of the technique is presented using results from a published model, with recommendations made on best practice. While the technique presented remains essentially arbitrary, it does give a mechanism for assessing the level of simulation error, and thus represents an advance over current practice of a round number of simulations with no assessment of model convergence

Voretigene Neparvovec for Treating Inherited Retinal Dystrophies Caused by RPE65 Gene Mutations: An Evidence Review Group Perspective of a NICE Highly Specialised Technology Appraisal

The UK National Institute for Health and Care Excellence (NICE) considered evidence for voretigene neparvovec (VN; Luxturna®) for the treatment of RPE65-mediated inherited retinal dystrophies (IRD) within its highly specialised technology programme. This paper summarises the evidence provided by the company; the appraisal of the evidence by the Peninsula Technology Appraisal Group, who were commissioned to act as the independent evidence review group (ERG); and the development of the NICE guidance by the appraisal committee. The evidence presented by the company highlighted the significant lifelong burden of IRD for patients and carers. Evidence to support the effectiveness of VN was lacking, but the available evidence showed a modest, sustained improvement across a variety of vision-related outcomes. While patients would remain visually impaired, the committee considered that VN would prevent further deterioration in vision. The modelling approach used by the company had a number of limitations and relied heavily upon a large volume of clinical expert input to produce cost-effectiveness estimates with large uncertainty around long-term effectiveness. The ERG’s main concerns revolved around these long-term outcomes and the plausibility of utility values. The NICE committee were convinced that the clinical benefits of VN were important and an appropriate use of national health service resources within a specialised service. The committee concluded that a high unmet need existed in patients with RPE65-mediated IRD and that VN represents a step change in the management of this condition

Cost–effectiveness analysis for avelumab first-line maintenance treatment of advanced urothelial carcinoma in Scotland

Aim: The cost–effectiveness of avelumab first-line maintenance treatment for locally advanced or metastatic urothelial carcinoma in Scotland was assessed. Materials & methods: A partitioned survival model was developed comparing avelumab plus best supportive care (BSC) versus BSC alone, incorporating JAVELIN Bladder 100 trial data, costs from national databases and published literature and clinical expert validation of assumptions. Incremental cost–effectiveness ratio (ICER) was estimated using lifetime costs and quality-adjusted life-years (QALY). Results: Avelumab plus BSC had incremental costs of £9446 and a QALY gain of 0.63, leading to a base-case (deterministic) ICER of £15,046 per QALY gained, supported by robust sensitivity analyses. Conclusion: Avelumab first-line maintenance is likely to be a cost-effective treatment for locally advanced or metastatic urothelial carcinoma in Scotland

MPES-R: Multi-Parameter Evidence Synthesis in R for Survival Extrapolation-A Tutorial

Survival extrapolation often plays an important role in health technology assessment (HTA), and there are a range of different approaches available. Approaches that can leverage external evidence (i.e. data or information collected outside the main data source of interest) may be helpful, given the extent of uncertainty often present when determining a suitable survival extrapolation. One of these methods is the multi-parameter evidence synthesis (MPES) approach, first proposed for use in HTA by Guyot et al., and more recently by Jackson. While MPES has potential benefits over conventional extrapolation approaches (such as simple or flexible parametric models), it is more computationally complex and requires use of specialist software. This tutorial presents an introduction to MPES for HTA, alongside a user-friendly, publicly available operationalisation of Guyot's original MPES that can be executed using the statistical software package R. Through two case studies, both Guyot's and Jackson's MPES approaches are explored, along with sensitivity analyses relevant to HTA. Finally, the discussion section of the tutorial details important considerations for analysts considering use of an MPES approach, along with potential further developments. MPES has not been used often in HTA, and so there are limited examples of how it has been used and perceived. However, this tutorial may aid future research efforts exploring the use of MPES further

sj-docx-1-mdm-10.1177_0272989X231168618 – Supplemental material for A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment

Supplemental material, sj-docx-1-mdm-10.1177_0272989X231168618 for A Systematic Review of Methods to Incorporate External Evidence into Trial-Based Survival Extrapolations for Health Technology Assessment by Ash Bullement, Matthew D. Stevenson, Gianluca Baio, Gemma E. Shields and Nicholas R. Latimer in Medical Decision Making</p