- •Standard methods to handle missing data in trial-based economic evaluations discard some of the observed responses. In this article, we illustrate how joint longitudinal models provide an alternative and potentially less biased approach for handling missing data with respect to current practice under a missing at random assumption.
- •Methods that ignore some of the available information may be associated with biased results and mislead the decision-making process. Given the common problem of missing data, many study conclusions could be based on imprecise economic evidence.
- •This is a potentially serious issue for those who use these evaluations in their decision making, thus possibly leading to incorrect policy decisions about the cost-effectiveness of new treatment options.
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