Correlated longitudinal and time-to-event outcomes are frequent (co-)primary and key secondary endpoints in randomized clinical trials (RCTs). Despite their biological associations, these types of data are often analyzed separately, leading to loss of information and increases in bias. In this talk, we set out how joint modeling of longitudinal and time-to-event endpoints can be used in RCTs to answer various research questions. The key concepts of joint models are introduced and illustrated for a completed trial in ALS. The output of a joint model can be used to answer different clinically relevant research questions, where the interpretation of effect estimates and those obtained from conventional methods are similar. Albeit joint models have the potential to overcome the limitations of commonly used alternatives, they require additional assumptions regarding the distributions, as well as the associations between two endpoints. Improving the uptake of joint models in RCTs may start by outlining the exact research question one seeks to answer, thereby determining how best to prespecify the model and defining the parameter that should be of primary interest.