Daniel Sugrue | 30/01/2026
Across 2025, we contributed 25 publications in 14 journals and presented 21 studies across scientific meetings in cardiology, immunology, microbiology, nephrology, respiratory medicine, oncology, diabetes, obesity, and rare diseases.
Across methodological innovation, dynamic population modelling, advanced survival analysis, real-world evidence, and structured expert elicitation, the work reflects a broad effort to address methodological limitations, clarify uncertain evidence bases, and improve the quality of information available for decision making. Below is a summary of key contributions. The full list of publications is available here.
1. Development of a modelling framework for composite endpoints in HTA
Composite endpoints are increasingly used in clinical trials but often pose interpretative and methodological challenges for health technology assessment (HTA). Heterogeneous event types within a single endpoint can obscure treatment effects and complicate their incorporation into economic models.
This year, we proposed a structured framework for analysing and modelling composite endpoints that improves transparency and interpretability. Across two publications, we demonstrated how component events can be handled more consistently and how external data sources may be incorporated to support more robust modelling.
Significance:
More rigorous approaches to composite endpoints reduce ambiguity and support clearer translation of trial data into HTA contexts. This enables more consistent modelling and provides decision-makers with evidence that better reflects underlying clinical patterns.
2. Application of system dynamics to explore obesity as a complex system
Obesity is influenced by interactions among biological, behavioural, environmental, demographic, and policy-level factors. Traditional epidemiological or economic models often struggle to represent these interdependencies and long-term feedback mechanisms.
Using a system dynamics approach, we developed a model that captures these dynamic relationships and illustrates how obesity prevalence may change over future decades. The work also identifies points within the system where prevention strategies may have the greatest influence. The study received substantial attention within the public health modelling community and became the most-cited article in JME in 2025.
Significance:
Understanding obesity as a complex system supports more targeted, mechanism-based intervention planning. This approach facilitates longer-term strategic decision-making rather than reactive responses to rising prevalence.
3. Enhancing long-term survival modelling in ATTR-CM
Long-term survival extrapolation in high-mortality diseases such as ATTR-CM is subject to significant uncertainty. Standard parametric models may yield implausible predictions, diverging from clinical expectations and demographic realities.
Our work examined the utility of relative-survival methods in this context and showed that aligning patient-level risk with population mortality trends can produce more credible, clinically coherent projections. This supports more stable long-term evaluations in a therapeutic area where uncertainty has historically been substantial.
Significance:
More reliable survival projections enable more defensible assessments of long-term value, reducing the influence of modelling artefacts in areas with inherently uncertain outcomes.
4. Consolidating fragmented evidence in EGPA
Evidence on eosinophilic granulomatosis with polyangiitis (EGPA) is limited and heterogeneous, with studies varying in definitions, methods, and reporting. This fragmentation has hindered efforts to characterise disease burden and progression.
Our systematic review and meta-analysis synthesised this distributed evidence to produce a clearer and more standardised understanding of EGPA epidemiology. The consolidated findings improve the foundation for clinical decision-making, economic evaluations, and future research.
Significance:
A more coherent evidence base supports stronger study designs and more valid model inputs, reducing uncertainty for clinicians, researchers, and policy-makers.
5. Generating structured expert insight for LAD-I through Delphi methods
LAD-I is an ultra-rare disease with limited empirical evidence due to extremely small global patient numbers. As a result, critical questions regarding disease burden, progression, and unmet need are difficult to answer through traditional research.
Through a structured Delphi study, we collected and synthesised perspectives from clinicians experienced in managing LAD-I. This process provided insight into disease trajectories and burden that cannot be obtained through existing datasets.
Significance:
In contexts where empirical data are scarce, structured expert elicitation is essential for informing study design, care pathways, and policy decisions.
A note to our collaborators
These outputs were made possible through collaboration among clinicians, academics, analysts, statisticians, writers, project managers, reviewers, and sponsors. Their collective expertise ensured that the resulting evidence was rigorous, transparent, and suited to informing decisions in areas where clarity is urgently needed.
We look forward to continuing this work in 2026 and contributing to further evidence generation and applied research.