Health state utility (HSU) estimates are among the most important and uncertain data inputs in cost-utility models which are increasingly being used to inform health technology assessment, pricing, and reimbursement decisions in many countries. This course will provide an in-depth consideration of best practice in the collection of health utility data in clinical trials, real-world and other studies, to provide high quality HSU estimates appropriate for economic modeling. Centered on the ISPOR Outcomes Research Guideline, Collecting Health-State Utility Estimates for Economic Models in Clinical Studies (Wolowacz et al., 2016), the course will address key challenges surrounding study design, data collection and analysis. This will include how to anticipate and address common issues that may affect data quality, alignment with the needs of economic model, acceptability to the model audience, and how to apply good research practices for HSU estimation in future research.
The course will also address issues associated with collection of utility data for rare diseases and from special populations including cognitively impaired and pediatric populations. The course will be of value for researchers actively involved in the design or implementation of HSU data collection or analysis, those involved in patient-reported outcomes research, economic modeling, economic evaluation, or health technology assessment. The course will not cover in any depth the fundamentals of utility theory, development of generic or condition-specific preference-based multi-attribute utility instruments, or how to perform time trade-off or standard gamble experiments. Nor will it cover statistical methods for mapping/cross-walking from a condition-specific HRQL measure. Although these topics will be touched on in overview, the focus of this course will be on optimizing the collection of utility data to provide HSU estimates for economic models.
