Modelling batched Gaussian longitudinal weight data in mice subject to informative dropout

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.