The Impact of the Approach to Accounting for Age and Sex in Economic Models on Predicted Quality-Adjusted Life-Years.

Background: The method used to model general population mortality estimates in cohort models can make a meaningful difference in appraisals; particularly in scenarios involving potentially curative treatments where a prior National Institute for Health and Care Excellence (NICE) appraisal demonstrated that this assumption alone could make a difference of ~£10,000 to the incremental cost-effectiveness ratio.

Objective: Our objective was to evaluate the impact of different methods for calculating general population mortality estimates on the predicted total quality-adjusted life expectancy (QALE) as well as absolute and proportional quality-adjusted life year (QALY) shortfall calculations.

Methods: We employed three distinct methods for deriving general population mortality estimates: firstly, utilizing the population mean age at baseline; secondly, modelling the distribution of mean age at baseline by fitting a parametric distribution to patient-level data sourced from the Health Survey for England (HSE); and thirdly, modelling the empirical age distribution.

Modelling Treatment Sequences in Immunology: Optimizing Patient Outcomes.

Introduction: For some immune-mediated disorders, despite the range of therapies available there is limited evidence on which treatment sequences are best for patients and healthcare systems. We investigated how their selection can impact outcomes in an Italian setting.

Methods: A 3-year state-transition treatment-sequencing model calculated potential effectiveness improvements and budget reallocation considerations associated with implementing optimal sequences in ankylosing spondylitis (AS), Crohn's disease (CD), non-radiographic axial spondyloarthritis (NR-AxSpA), plaque psoriasis (PsO), psoriatic arthritis (PsA), rheumatoid arthritis (RA), and ulcerative colitis (UC).

R and Shiny for Cost-Effectiveness Analyses: Why and When? A Hypothetical Case Study.

Introduction: Health economics models are typically built in Microsoft Excel owing to its wide familiarity, accessibility and perceived transparency. However, given the increasingly rapid and analytically complex decision-making needs of both the pharmaceutical industry and the field of health economics and outcomes research (HEOR), the demands of cost-effectiveness analyses may be better met by the programming language R.

Objective: This case study provides an explicit comparison between Excel and R for contemporary cost-effectiveness analysis.

Modelling Survival of Patients Treated with Adjuvant Nivolumab Who Have Melanoma with Lymph Node Involvement or Metastatic Disease After Complete Resection.

Introduction: Nivolumab demonstrated significant recurrence-free survival (RFS) gains versus ipilimumab in the CheckMate-238 trial, whereas the CA184-029 trial showed superior RFS gains for ipilimumab versus placebo. No head-to-head trial data were available to compare the efficacy of nivolumab to that of observation, so indirect treatment comparisons were required. Additionally, overall survival (OS) data were not available from CheckMate-238, and the clinical pathway for melanoma has changed significantly over the last decade.