title: Modelling batched Gaussian longitudinal weight data in mice subject to informative dropout.
abstract: 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.
authors.affiliation: Biostatistics and Bioinformatics Branch, Division of Epidemiology, Statistics, and Prevention Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, MD, USA albertp@mail.nih.gov.
authors.forename: Paul S
authors.initials: PS
authors.lastname: Albert
authors.affiliation: Biometrics Research Branch, Division of Cancer Treatment and Diagnosis, National Cancer Institute, Bethesda, MD, USA.
authors.forename: Joanna H
authors.initials: JH
authors.lastname: Shih
mesh_terms: D000818:Animals; D001835:Body Weight; D003627:Data Interpretation, Statistical; D003643:Death; D008137:Longitudinal Studies; D051379:Mice; D015233:Models, Statistical; D016011:Normal Distribution