Missing data is a problem commonly encountered in health intervention studies. It is particularly prevalent in palliative care research where collection of complete data is often hampered by the deteriorating health and sometimes death of the study participants. Missing data is a limitation for the validity of an economic evaluation. If missing data is not dealt with appropriately during analysis, there is a risk of bias occurring in the estimates of the outcomes, both benefits and costs, associated with the interventions being compared. Such biased results would in turn effectively invalidate any inferences made to guide the decision-making process. Given the importance of dealing properly with missing data to making correct inferences from study results, we present here a guide paying specific attention to problems that might arise when studying end-of-life populations in a health economic setting. We begin by describing what it means for data to be missing and how this affects inference. We then outline methods for dealing with different types of missing data, progressing from simple (and often unsatisfactory) methods such as complete-case analysis to more sophisticated and computationally expensive approaches such as multiple imputation, which generally produce more meaningful results. We finish by considering specific issues around end-of-life populations.