Modelling longitudinal data subject to informative dropout is an active area in statistical research. This article focuses on modelling such longitudinal data when the outcome at each follow-up time is collected in batches rather than individually collected. The problem occurred in a study that compared the weight of mice over time between a control and a treatment group, where animal weight was measured in batches of five animals per cage. We develop both a shared parameter and a pattern mixture modelling approach for accounting for potentially informative dropout due to an animal’s death. Our methodology suggests that animals receiving the treatment have a lower weight in mid-life, and have a slower decline in weight in the later period of life. Our simulations suggest that both the shared random parameter and pattern mixture modelling approaches work well under a correctly specified model. However, the pattern mixture model is more robust against model misspecification than the shared random parameter model, but the shared random parameter model parameters have a more direct interpretation than those of the pattern mixture modelling approach.