- Extended Cost-Effectiveness Analysis (ECEA)
One area of methodological development is using ECEA frameworks to evaluate equity impacts. ECEA expands traditional cost-effectiveness analysis to consider distribution of health outcomes and financial risks across population subgroups.
- Distributional Cost-Effectiveness Analysis (DCEA)
DCEA is a framework I employ to conduct equity-focused economic evaluations of health interventions. It extends traditional cost-effectiveness analysis by accounting for how costs and outcomes are distributed across different groups in the population.
Research Methodology Development
Continuous improvement of evaluation methods is important to generate robust evidence to inform equitable resource prioritization and policy-making. In this domain, my lab is engaged in two key projects:
Meta-analysis in Economic Evaluation (MAEE)
The MAEE project aims to extend meta-analytic techniques to synthesize cost-effectiveness evidence across multiple studies. This helps quantify uncertainty and transfers learning on effective interventions. It is particularly useful for under-researched patient populations.
TIP Framework for Evidence Synthesis
The TIP (Theme, Intensity, Provider/Platform) framework developed by my team provides a standardized approach for synthesizing evidence from diverse sources beyond clinical trials. It assists guideline development by mapping multipronged interventions and delivery platforms on core themes.
Both MAEE and TIP place emphasis on mapping equity outcomes and understanding what works best for whom. This patient-centered orientation aligns with my research vision of advancing health economics methods to reduce disparities and maximize well-being for all.
Value Assessment Framework
A key area of my research is strengthening value assessment approaches to incorporate multiple dimensions of value beyond conventional cost-effectiveness analysis.
Augmented CEA, using DCE
One methodology applied in developing a Value Assessment Framework is augmented CEA where discrete choice experiments (DCE) are used to quantify patient and societal preferences. Attributes such as treatment outcomes, costs and side effects are valued to generate composite scores. These scores capture value judgments not reflected in monetary or QALY metrics alone.
An aim is to provide policy relevant evidence on maximizing health system value and equity in specific clinical contexts like cancer, maternal health and antimicrobial resistance. Several studies are also underway in these therapy areas.