The Health Economics & Decision Modelling Lab generates policy-relevant evidence to support better health-care decisions. We integrate health economic evaluation, decision-analytic modelling, and real-world evidence (RWE) to assess the value of medicines, medical devices, and health services—answering practical questions about what works, for whom, at what cost, and under what conditions.

Our work spans the full evidence pathway: from defining decision problems with stakeholders, to building transparent models, to quantifying uncertainty, and to translating results into actionable insights for pricing, reimbursement, and resource allocation. We also emphasize patient-centred outcomes, including patient-reported outcomes (PROs), health-related quality of life (HRQoL), and health utility, to ensure that evaluations reflect outcomes that matter to patients and the public.

Research themes

• Economic evaluation & HTA: cost-effectiveness/cost-utility analysis and budget impact analysis for medicines, devices, and services

• Decision modelling: decision trees, state-transition models (e.g., Markov), microsimulation, and uncertainty/sensitivity analyses

• Real-world evidence & outcomes research: healthcare big data–based effectiveness, safety, utilization, and cost studies

• PROs, HRQoL & health utility: measurement, valuation, and application of patient-reported outcomes in economic evaluation

• Pricing & reimbursement policy: evidence generation to inform coverage decisions and value-based policy design
  
  

We collaborate with academia, healthcare providers, public agencies, and industry partners to co-produce rigorous, transparent, and decision-ready evidence.

Study

• Health economics foundations & policy context

Covers the core economic concepts used in healthcare and how they translate into policy decisions—why health systems need prioritisation, how incentives and market failures shape outcomes, and how economics supports resource allocation under budget constraints.

• Economic evaluation methods (CEA/CUA) and interpretation

Teaches how to design and interpret economic evaluations of health technologies and services, including cost-effectiveness and cost-utility analysis, and how results are used within real decision-making guidelines and reimbursement processes.

• Cost-effectiveness modelling for HTA (decision trees, Markov/state-transition models)

Focuses on building decision-analytic models to estimate long-term costs and outcomes when data are incomplete or follow-up is limited—structuring decision problems, selecting model types, populating input values, and critically appraising model assumptions and outputs.

• Research methods, study design & systematic review skills

Builds practical skills in planning and evaluating health research, including quantitative/qualitative approaches and systematic review methods, with emphasis on producing evidence suitable for health technology evaluation (evaluating clinical evidence and combining results across studies—understanding RCT design and analysis, conducting meta-analysis, using frequentist and Bayesian approaches)

• Health outcomes valuation (QALYs, utilities, preference-based measures; beyond-QALY issues)

Explains how health benefits are measured and valued for economic evaluation—utility measurement, preference-based instruments, deriving quality-adjustment weights for QALYs, and key debates such as capturing wider (non-health) benefits.

• Advanced statistical methods for cost-effectiveness analysis (cost data, time-to-event, observational data)

Trains you to analyse common data types in applied health economics—skewed cost data, survival/time-to-event outcomes, and treatment effects from observational datasets—so estimates are credible for modelling and decision-making.

• Advanced simulation modelling (including discrete-event simulation; model validation and interpretation)

Introduces simulation approaches for complex pathways and constrained resources—developing and validating simulation models (notably discrete-event simulation), exploring system dynamics, and interpreting outputs to inform policy and operational decisions.