Electronic health records are an increasingly valuable source of longitudinal data, and have been used both in observational studies and in (cluster-) randomized trials. A common feature of such data is that the follow-up times are irregular, and potentially correlated with the outcome of interest. This talk will review recent developments that allow for inference in the presence of informative observation times, with a particular focus on multiple outputation, clustered longitudinal data, and Bayesian inference