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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). Sequences included three biological or disease-modifying treatments, followed by best supportive care. Disease-specific response measures were selected on the basis of clinical relevance, data availability, and data quality. Efficacy was differentiated between biologic-naïve and experienced populations, where possible, using published network meta-analyses and real-world data. All possible treatment sequences, based on reimbursement as of December 2022 in Italy (analyses' base country), were simulated. RESULTS: Sequences with the best outcomes consistently employed the most efficacious therapies earlier in the treatment pathway. Improvements to prescribing practice are possible in all diseases; however, most notable was UC, where the per-patient 3-year average treatment failure was 37.3% higher than optimal. The results focused on the three most crowded and prevalent immunological sub-condition diseases in dermatology, rheumatology, and gastroenterology: PsO, RA, and UC, respectively. By prescribing from within the top 20% of the most efficacious sequences, the model found a 15.1% reduction in treatment failures, with a 1.59% increase in drug costs. CONCLUSIONS: Prescribing more efficacious treatments earlier provides a greater opportunity to improve patient outcomes and minimizes treatment failures.

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The annual instructional virtual team Project X brings together professors and students from across the globe to engage in client projects. The 2020 project was challenged by the global disruption of the COVID-19 pandemic. This paper draws on a quantitative dataset from a post-project survey among 500 participating students and a qualitative narrative inquiry of personal experiences of the faculty members. The findings reveal how innovative use of a variety of collaboration and communication technologies helped students and their professors in building emotional connection and compassion to support each other in the midst of the crisis, and to accomplish the project despite connectivity disruptions. The results suggest that the role of an instructor changed to a coach and mentor, and technology was used to create a greater sense of inclusion and co-presence in student-faculty interactions. Ultimately, the paper highlights the role of technology to help the participants navigate sudden crisis affecting a global online instructional team project. The adaptive instructional teaching strategies and technologies depicted in this study offer transformative potential for future developments in higher education.

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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. METHODS: We constructed duplicate cost-effectiveness models using Excel and R (with a user interface built using the Shiny package) to address a hypothetical case study typical of contemporary health technology assessment. RESULTS: We compared R and Excel versions of the same model design to determine the advantages and limitations of the modelling platforms in terms of (i) analytical capability, (ii) data safety, (iii) building considerations, (iv) usability for technical and non-technical users and (v) model adaptability. CONCLUSIONS: The findings of this explicit comparison are used to produce recommendations for when R might be more suitable than Excel in contemporary cost-effectiveness analyses. We conclude that selection of appropriate modelling software needs to consider case-by-case modelling requirements, particularly (i) intended audience, (ii) complexity of analysis, (iii) nature and frequency of updates and (iv) anticipated model run time.

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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. Four modelling options were developed using different methods and evidence sources to estimate OS and the impact of nivolumab on predicted life-years in the adjuvant setting; however, this article focuses on two primary methods. METHODS: RFS for nivolumab and observation were informed by a patient-level data meta-regression. The first model was a partitioned survival model, where the parametric OS curve for observation was derived from CA184-029 and nivolumab OS was based on a surrogacy relationship between RFS and OS specific to adjuvant melanoma. The other option used a state-transition model to estimate post-recurrence survival using different data sources. RESULTS: The modelling options estimated different OS for both nivolumab and observation but demonstrated at least a 32% increase in life-years gained for nivolumab versus observation. CONCLUSION: This analysis demonstrated the difficulties in modelling within the adjuvant setting. Each model produced different survival projections, showing the need to explore different techniques to address the extent of uncertainty. This also highlighted the importance of understanding the impact of RFS in the long term in a setting where the aim of treatment is to remain disease free.