Objectives: How to perform an intention to treat (ITT) analysis when a patient has a baseline value but no follow-up measurements is problematic. The purpose of this study was to compare different methods that deal with this problem, i.e. no imputation (standard and alternative mixed model analysis), single imputation (i.e. baseline value carried forward), and multiple imputation (selective and non-selective). Study design and setting: We used a simulation study with different scenarios regarding 1) the association between missingness and the baseline value, 2) whether the patients did or did not receive the treatment, and 3) the percentage of missing data, and two real life data sets. Results: Bias and coverage were comparable between the two mixed model analyses and multiple imputation in most situations including the real life data examples. Only in the situation when the patients in the treatment group were simulated not to have received the treatment, selective imputation using this information outperformed all other methods. Conclusions: In most situations a standard mixed model analysis without imputation is appropriate as ITT analysis. However, when patients with missing follow-up data allocated to the treatment group did not received treatment, it is advised to use selective imputation, using this information, although the results should be interpreted with caution.