Objective: To systematically explore a series of issues related to missing data in health economics evaluation based on individual level data, providing references for correctly handling missing data in practical research. Methods: Based on reviewing the basic of missing data, it summarized the causes, types, and handling methods of missing data in health economics evaluation based on individual level data. Results: Multiple-imputation is the most common method for handling missing data. In the application of the method, care should be taken to choose the specific method of interpolation, the type of interpolation modelling and the variables of the interpolation model wisely. Conclusion: There is no universal agreement quality standard for the handling and reporting of miss- ing data in health economic evaluation, which needs to be further explored in the future.