Missing data are ubiquitous in health economic evaluation. The major concern is that individuals with missing information tend to be systematically different from those with complete data in a way that is related to the decision problem at hand. As a result, cost-effectiveness inferences based on complete cases are often misleading. These concerns face health economic evaluation based on a single study, and studies that synthesise data from several sources in decision models. While accessible, appropriate methods for addressing the missing data are available in most software packages, their uptake in health economic evaluation has been limited.
Taught by leading experts in missing data methodology, this course offers an in-depth description of both introductory and advanced methods for addressing missing data in economic evaluation. These will include multiple imputation, maximum likelihood, hierarchical approaches, Bayesian analysis, and sensitivity analysis strategies using pattern mixture models and selection models. The course will introduce the statistical concepts and underlying assumptions of each method, and provide extensive guidance on the application of the methods in practice. Participants will engage in practical sessions illustrating how to implement each technique with user-friendly software (Stata and R).
At the end of the course, the participants should be able to develop an entire strategy to address missing data in health economic studies, from describing the problem, to choosing an appropriate statistical approach, to conducting sensitivity analysis to standard missing data assumptions, to interpreting the cost-effectiveness results in light of those assumptions. We welcome participants bringing their own data and problems, and one session is dedicated to discussion of participants’ case-studies.