Studies with longitudinally measured outcomes are often plagued by missing data due to patients withdrawing before completing the measurement schedule. Dropout is defined when sequences of longitudinal measurements on some patients  terminate prematurely. Often the reasons for dropout are informative or non-ignorable. However, the standard methods for analysing longitudinal outcome data assume that missingness is non-informative and also ignore the reasons for dropout, which could result in a biased comparison between the covariate groups. 

We propose a joint model that consists of a linear mixed effects submodel for the longitudinal outcome, and cause-specific hazard sub-models for competing reasons of dropout, linked together by latent processes. The proposed method is studied in simulations and applied to the MAGNETIC trial; the largest randomised placebo-controlled study to date comparing the addition of nebulised magnesium sulphate to standard treatment in acute severe asthma in children. The reasons for dropout are sometimes clearly known and recorded during the MAGNETIC trial, but in many instances these reasons are unknown or unclear. We explore the impact of the MAGNETIC dropout process on evaluation of the treatment effect, and jointly model the longitudinal outcome of Asthma Severity Score and informative dropout process to incorporate the information regarding the reasons for dropout by treatment group.