In drug development for patients with rare disease, small clinical trials are usually conducted. It is critical to make a well-considered statistical analysis plan on how to deal with dropouts in such clinical trials, because the impact of missing data is relatively large. In this research we focused on longitudinal response with dropout in small clinical trials, where complex statistical analysis methods do not work well, due to small sample size relative to the number of time-points. We evaluated various imputation methods (single imputation; last observation carried forward (LOCF), and some multiple imputation (MI) models (marginal and conditional imputation)) under different missing mechanisms. An example motivated by an actual clinical trial was used in the evaluations. Simulation studies were performed for continuous responses under various settings including sample size, the number of time-points, hierarchical structures, missing mechanisms, missing fractions, and analysis models. The details of results will be shown in the presentation.