An outstanding methodological concern in economic evaluation studies is that there may be missing data, for example, because patients are lost to follow-up or fail to respond to quality-of-life or resource use questionnaires. Missing data may be problematic because individuals with missing information tend to be systematically different from those with complete data. Most published studies fail to address this issue and report cost-effectiveness inferences based solely on the complete cases. Inappropriate methods will lead to biased results, and ultimately can affect the decision of whether an intervention should be prioritised.
While standard multiple imputation methods have been proposed for handling missing data in cost-effectiveness analysis, these may be insufficient in many settings. For example, they assume that individual observations are independent (which may be implausible in multicentre studies or meta-analysis of individual-participant data) or that the imputation model is correctly specified. In addition, the methods proposed assumed that data are missing at random, i.e. the probability of missingness is only conditional on the observed data. However, the probability of missing costs or outcomes may depend on unobserved values, i.e. data may be missing not at random.
THETA is focusing on the following areas:
- Comparing different approaches for dealing with clustering when addressing missing data in CEA.
- Assessing novel ‘doubly robust’ approaches that minimise the reliance on model specification when handling missing data in CEA that use non-randomised studies.
- Delineating strategies for dealing with cost-effectiveness data missing not at random.
- Developing tools to elicit expert opinion to inform sensitivity analysis to assumptions about the missing data in CEA studies.
- Investigating how untestable assumptions about data missing not at random link multiple imputation and Heckman selection models.