Joint modelling allows us to investigate the inter-relationships between a repeatedly measured longitudinal outcome, such as a biomarker measured with error, and the time to an event of interest, such as mortality. The field has gained tremendous popularity over recent years, as it can both provide an efficient way of accounting for informative drop-out in longitudinal studies, and quantify the impact of a time-varying covariate on survival, leading to development of dynamic risk prediction tools. Extensions to the framework are constantly being developed as the methods become more widely utilised in fields such as cancer and cardiovascular research. In this talk, I will give an overview of the joint model framework, showing the benefits it offers over more simplistic approaches, and discuss some of the more recent extensions including allowing for competing risks and multiple longitudinal outcomes. Given the rapidly growing use, it’s important to understand and assess the fundamental assumptions of the framework, which I argue has received less attention. Finally, I’ll describe the development of an overarching general implementation in both Stata and R, which makes both existing joint models and many extensions, very simple to derive and apply.