Missing values commonly complicate the analysis of observational data. Multiple imputation (MI) is considered the “gold standard” for handling incomplete covariates. MI, developed at the beginning of the Computer Age, is based on Bayesian ideas. In complex settings, e.g. involving non-linear associations or multi-level data, the assumptions of the commonly used MI algorithms are, however, often violated, leading to possibly biased results. Thanks to the current computational power, a fully Bayesian approach, allowing us to simultaneously estimate parameters of interest and impute missing values, is now feasible. This approach is theoretically valid and superior to MI in complex settings. Highly complex non-standard missing data models can relatively easily be implemented with the help of freely available software such as the R package JointAI. In this course, we briefly review the essentials of multi-level data, Bayesian concepts and (multiple) imputation. The main focus is on the Bayesian approach to missing values in covariates in multi-level and longitudinal studies, which is motivated and illustrated using examples from clinical and epidemiological studies. Practical sessions will be organized to show the capabilities of the R package JointAI, starting with simpler standard settings and extending to highly complex joint models for longitudinal and survival data and imputation in non-standard settings.